332 Publications

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[332]
2017 | Conference Paper | PUB-ID: 2913752
Göpfert JP, Göpfert C, Botsch M, Hammer B (Accepted)
Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction.
In: Proceedings of IEEE Symposium Series on Computational Intelligence. .
PUB
 
[331]
2017 | Conference Paper | PUB-ID: 2908201
Göpfert C, Pfannschmidt L, Hammer B (2017)
Feature Relevance Bounds for Linear Classification.
In: Proceedings of the ESANN. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
PUB | Files available
 
[330]
2017 | Journal Article | PUB-ID: 2913389
Paaßen B, Hammer B, Price T, Barnes T, Gross S, Pinkwart N (Submitted)
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces.
Journal of Educational Datamining.
PUB | arXiv
 
[329]
2017 | Journal Article | PUB-ID: 2911900
Paaßen B, Göpfert C, Hammer B (2017)
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces.
Neural Processing Letters: 1-21.
PUB | DOI | arXiv
 
[328]
2017 | Conference Paper | PUB-ID: 2909037
Prahm C, Schulz A, Paaßen B, Aszmann O, Hammer B, Dorffner G (2017)
Echo State Networks as Novel Approach for Low-Cost Myoelectric Control.
In: Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). ten Telje A, Popow C, Holmes JH, Sacchi L (Eds); Lecture Notes in Computer Science, 10259. Springer: 338--342.
PUB | Files available | DOI
 
[327]
2017 | Journal Article | PUB-ID: 2909372
Schulz A, Brinkrolf J, Hammer B (2017)
Efficient Kernelization of Discriminative Dimensionality Reduction.
Neurocomputing accepted.
PUB | PDF | DOI
 
[326]
2017 | Conference Paper | PUB-ID: 2909369
Paaßen B, Schulz A, Hahne J, Hammer B (2017)
An EM transfer learning algorithm with applications in bionic hand prostheses.
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.
PUB | PDF
 
[325]
2017 | Conference Paper | PUB-ID: 2909371
Biehl M, Hammer B, Villmann T (2017)
Prototype based models for the supervised learning of classificaton schemes.
In: Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016. in press.
PUB
 
[324]
2017 | Conference Paper | PUB-ID: 2909370
Frenay B, Hammer B (2017)
Label-Noise-Tolerant Classification for Streaming Data.
In: IEEE International Joint Conference on Neural Neworks. .
PUB
 
[323]
2016 | Conference Paper | PUB-ID: 2900676
Paaßen B, Göpfert C, Hammer B (2016)
Gaussian process prediction for time series of structured data.
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges: 41-46.
PUB | PDF
 
[322]
2016 | Conference Paper | PUB-ID: 2905855
Paaßen B, Schulz A, Hammer B (2016)
Linear Supervised Transfer Learning for Generalized Matrix LVQ.
In: Proceedings of the Workshop New Challenges in Neural Computation 2016. Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports, 11-18.
PUB
 
[321]
2016 | Conference Paper | PUB-ID: 2907624
Losing V, Hammer B, Wersing H (2016)
Choosing the Best Algorithm for an Incremental On-line Learning Task.
Presented at the European Symposium on Artificial Neural Networks, Brügge.
PUB | PDF
 
[320]
2016 | Conference Paper | PUB-ID: 2907622
Losing V, Hammer B, Wersing H (2016)
KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift.
In: 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE: 291-300.
PUB | PDF | DOI
 
[319]
2016 | Conference Paper | PUB-ID: 2909367
Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B (2016)
Local Reject Option for Deterministic Multi-class SVM.
In: Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. 251--258.
PUB | DOI
 
[318]
2016 | Conference Paper | PUB-ID: 2909366
Villmann T, Kaden M, Bohnsack A, Villmann JM, Drogies T, Saralajew S, Hammer B (2016)
Self-Adjusting Reject Options in Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016. Merényi E, Mendenhall MJ, O'Driscoll P (Eds); Advances in Intelligent Systems and Computing, 428. Springer International Publishing: 269-279.
PUB | DOI
 
[317]
2016 | Journal Article | PUB-ID: 2910957
Biehl M, Hammer B, Villmann T (2016)
Prototype-based models in machine learning.
Wiley Interdisciplinary Reviews: Cognitive Science 7(2): 92-111.
PUB | DOI
 
[316]
2016 | Conference Paper | PUB-ID: 2909368
Geppert er, Hammer B (2016)
Incremental learning algorithms and applications.
In: ESANN. .
PUB
 
[315]
2016 | Conference Paper | PUB-ID: 2909365
Brinkrolf J, Mittag T, Joppen R, Dr\ A, Pietsch K-H, Hammer B (2016)
Virtual optimisation for improved production planning.
In: New Challenges in Neural Computation. .
PUB
 
[314]
2016 | Journal Article | PUB-ID: 2907633
Lux M, Krüger J, Rinke C, Maus I, Schlüter A, Woyke T, Sczyrba A, Hammer B (2016)
acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data.
BMC Bioinformatics 17: 543.
PUB | PDF | DOI | WoS | PubMed | Europe PMC
 
[313]
2016 | Conference Paper | PUB-ID: 2908455
Losing V, Hammer B, Wersing H (2016)
Dedicated Memory Models for Continual Learning in the Presence of Concept Drift.
Presented at the Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona.
PUB | PDF
 
[312]
2016 | Conference Paper | PUB-ID: 2904909
Schulz A, Hammer B (2016)
Discriminative Dimensionality Reduction in Kernel Space.
In: ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016. i6doc.com.
PUB | PDF
 
[311]
2016 | Conference Paper | PUB-ID: 2904509
Paaßen B, Jensen J, Hammer B (2016)
Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming.
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
PUB
 
[310]
2016 | Conference Paper | PUB-ID: 2905729
Göpfert C, Paaßen B, Hammer B (2016)
Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning.
In: Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part 1. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9886. Springer Nature: 510-517.
PUB | PDF | DOI
 
[309]
2016 | Journal Article | PUB-ID: 2905193
Fischer L, Hammer B, Wersing H (2016)
Optimal local rejection for classifiers.
Neurocomputing 214: 445-457.
PUB | DOI | WoS
 
[308]
2016 | Conference Paper | PUB-ID: 2904178
Prahm C, Paaßen B, Schulz A, Hammer B, Aszmann O (2016)
Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift.
In: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Ibáñez J, Gonzáles-Vargas J, Azorín JM, Akay M, Pons JL (Eds); Springer: 153--157.
PUB | PDF | DOI
 
[307]
2016 | Conference Paper | PUB-ID: 2904469
Hosseini B, Hülsmann F, Botsch M, Hammer B (2016)
Non-Negative Kernel Sparse Coding for the Analysis of Motion Data.
Lecture Notes in Computer Science 9887: 506-514.
PUB | PDF | DOI
 
[306]
2016 | Conference Paper | PUB-ID: 2905195
Fischer L, Hammer B, Wersing H (2016)
Online Metric Learning for an Adaptation to Confidence Drift.
In: Proceedings of International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE: 748-755.
PUB
 
[305]
2016 | Journal Article | PUB-ID: 2783224
Paaßen B, Mokbel B, Hammer B (2016)
Adaptive structure metrics for automated feedback provision in intelligent tutoring systems.
Neurocomputing 192(SI): 3-13.
PUB | PDF | DOI | WoS
 
[304]
2016 | Journal Article | PUB-ID: 2903457
Schleif F-M, Hammer B, Gonzalez Monroy J, Gonzalez Jimenez J, Blanco-Claraco J-L, Biehl M, Petkov N (2016)
Odor recognition in robotics applications by discriminative time-series modeling.
PATTERN ANALYSIS AND APPLICATIONS 19(1): 207-220.
PUB | DOI | WoS
 
[303]
2015 | Conference Paper | PUB-ID: 2776021
Losing V, Hammer B, Wersing H (2015)
Interactive Online Learning for Obstacle Classification on a Mobile Robot.
Presented at the International Joint Conference on Neural Networks, Killarney, Ireland.
PUB | PDF | DOI
 
[302]
2015 | Conference Paper | PUB-ID: 2724156
Paaßen B, Mokbel B, Hammer B (2015)
Adaptive structure metrics for automated feedback provision in Java programming.
In: Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 307-312.
PUB | PDF
 
[301]
2015 | Journal Article | PUB-ID: 2710031
Mokbel B, Paaßen B, Schleif F-M, Hammer B (2015)
Metric learning for sequences in relational LVQ.
Neurocomputing 169: 306-322.
PUB | PDF | DOI | WoS
 
[300]
2015 | Conference Paper | PUB-ID: 2910954
Biehl M, Hammer B, Schleif F-M, Schneider P, Villmann T (2015)
Stationarity of Matrix Relevance LVQ.
In: 2015 International Joint Conference on Neural Networks (IJCNN). IEEE.
PUB | DOI
 
[299]
2015 | Journal Article | PUB-ID: 2909364
Hammer B, Toussaint M (2015)
Special Issue on Autonomous Learning.
{KI} 29(4): 323--327.
PUB | DOI
 
[298]
2015 | Conference Paper | PUB-ID: 2901612
Lux M, Sczyrba A, Hammer B (2015)
Automatic discovery of metagenomic structure.
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE).
PUB | DOI
 
[297]
2015 | Journal Article | PUB-ID: 2909226
Gisbrecht A, Hammer B (2015)
Data visualization by nonlinear dimensionality reduction.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5(2): 51-73.
PUB | DOI
 
[296]
2015 | Report | PUB-ID: 2901613
Lux M, Hammer B, Sczyrba A (2015)
Automated Contamination Detection in Single-Cell Sequencing.
Cold Spring Harbor Laboratory Press.
PUB | DOI
 
[295]
2015 | Conference Paper | PUB-ID: 2783165
Hosseini B, Hammer B (2015)
Efficient Metric Learning for the Analysis of Motion Data.
Presented at the Data Science and Advanced Analytics (DSAA), Paris, France.
PUB | PDF | DOI
 
[294]
2015 | Journal Article | PUB-ID: 2671047
Gisbrecht A, Schulz A, Hammer B (2015)
Parametric nonlinear dimensionality reduction using kernel t-SNE.
Neurocomputing 147: 71-82.
PUB | PDF | DOI | WoS
 
[293]
2015 | Journal Article | PUB-ID: 2772407
Nebel D, Hammer B, Frohberg K, Villmann T (2015)
Median variants of learning vector quantization for learning of dissimilarity data.
Neurocomputing 169: 295-305.
PUB | DOI | WoS
 
[292]
2015 | Journal Article | PUB-ID: 2759763
Schleif F-M, Zhu X, Hammer B (2015)
Sparse conformal prediction for dissimilarity data.
Annals of Mathematics and Artificial Intelligence 74(1-2): 95-116.
PUB | DOI | WoS
 
[291]
2015 | Conference Paper | PUB-ID: 2903777
Schulz A, Mokbel B, Biehl M, Hammer B (2015)
Inferring Feature Relevances From Metric Learning.
In: 2015 IEEE Symposium Series on Computational Intelligence. Institute of Electrical & Electronics Engineers (IEEE).
PUB | DOI
 
[290]
2015 | Preprint | PUB-ID: 2774656
Fischer L, Hammer B, Wersing H (2015)
Optimum Reject Options for Prototype-based Classification.
PUB | arXiv
 
[289]
2015 | Conference Paper | PUB-ID: 2774707
Fischer L, Hammer B, Wersing H (2015)
Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation.
In: ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 7-12.
PUB
 
[288]
2015 | Conference Paper | PUB-ID: 2774721
Fischer L, Hammer B, Wersing H (2015)
Combining Offline and Online Classifiers for Life-long Learning.
In: IJCNN, International Joint Conference on Neural Networks. 2808-2815.
PUB
 
[287]
2015 | Journal Article | PUB-ID: 2772413
Fischer L, Hammer B, Wersing H (2015)
Efficient rejection strategies for prototype-based classification.
Neurocomputing 169: 334-342.
PUB | DOI | WoS
 
[286]
2015 | Book Chapter | PUB-ID: 2900303
Schulz A, Hammer B (2015)
Visualization of Regression Models Using Discriminative Dimensionality Reduction.
In: Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, 9257. Springer Science + Business Media: 437-449.
PUB | PDF | DOI
 
[285]
2015 | Journal Article | PUB-ID: 2766822
Schulz A, Gisbrecht A, Hammer B (2015)
Using Discriminative Dimensionality Reduction to Visualize Classifiers.
Neural Processing Letters 42(1): 27-54.
PUB | PDF | DOI | WoS
 
[284]
2015 | Conference Paper | PUB-ID: 2900318
Schulz A, Hammer B (2015)
Metric Learning in Dimensionality Reduction.
In: Proceedings of the International Conference on Pattern Recognition Applications and Methods. Scitepress: 232-239.
PUB | DOI
 
[283]
2015 | Conference Paper | PUB-ID: 2900319
Schulz A, Hammer B (2015)
Discriminative dimensionality reduction for regression problems using the Fisher metric.
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE): 1-8.
PUB | DOI
 
[282]
2015 | Conference Paper | PUB-ID: 2900325
Blöbaum P, Schulz A, Hammer B (2015)
Unsupervised Dimensionality Reduction for Transfer Learning.
In: Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco: 507-512.
PUB | PDF
 
[281]
2015 | Journal Article | PUB-ID: 2752955
Walter O, Häb-Umbach R, Mokbel B, Paaßen B, Hammer B (2015)
Autonomous Learning of Representations.
KI - Künstliche Intelligenz: 1-13.
PUB | PDF | DOI
 
[280]
2015 | Conference Paper | PUB-ID: 2762087
Paaßen B, Mokbel B, Hammer B (2015)
A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems.
In: Proceedings of the 8th International Conference on Educational Data Mining. Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M (Eds); International Educational Datamining Society: 632-632.
PUB
 
[279]
2015 | Journal Article | PUB-ID: 2752948
Gross S, Mokbel B, Hammer B, Pinkwart N (2015)
Learning Feedback in Intelligent Tutoring Systems.
KI - Künstliche Intelligenz: 1-6.
PUB | PDF | DOI
 
[278]
2015 | Journal Article | PUB-ID: 2695196
Hofmann D, Gisbrecht A, Hammer B (2015)
Efficient approximations of robust soft learning vector quantization for non-vectorial data.
Neurocomputing 147: 96-106.
PUB | DOI | WoS
 
[277]
2014 | Journal Article | PUB-ID: 2694967
Jin Y, Hammer B (2014)
Computational Intelligence in Big Data.
IEEE Computational Intelligence Magazine 9(3): 12-13.
PUB | DOI | WoS
 
[276]
2014 | Conference Paper | PUB-ID: 2909361
Hammer B, Nebel D, Riedel M, Villmann T (2014)
Generative versus Discriminative Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, {WSOM} 2014, Mittweida, Germany, July, 2-4, 2014. 123--132.
PUB | DOI
 
[275]
2014 | Conference Paper | PUB-ID: 2909360
Gross S, Mokbel B, Hammer B, Pinkwart N (2014)
How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning.
In: Intelligent Tutoring Systems. Trausan-Matu S, Elizabeth Boyer K, E. Crosby M, Panourgia K (Eds); Lecture Notes in Computer Science, 8474. Springer: 340-347.
PUB
 
[274]
2014 | Conference Paper | PUB-ID: 2673548
Fischer L, Hammer B, Wersing H (2014)
Rejection strategies for learning vector quantization.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 41-46.
PUB
 
[273]
2014 | Conference Paper | PUB-ID: 2774498
Fischer L, Hammer B, Wersing H (2014)
Local Rejection Strategies for Learning Vector Quantization.
In: Artificial Neural Networks and Machine Learning – ICANN 2014. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa AEP (Eds); Lecture Notes in Computer Science, 8681. 563-570.
PUB | DOI
 
[272]
2014 | Conference Paper | PUB-ID: 2774643
Fischer L, Nebel D, Villmann T, Hammer B, Wersing H (2014)
Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches.
In: Advances in Self-Organizing Maps and Learning Vector Quantization. Villmann T, Schleif F-M, Kaden M, Lange M (Eds); Advances in Intelligent Systems and Computing, 295. 109-118.
PUB | DOI
 
[271]
2014 | Conference Paper | PUB-ID: 2900320
Frenay B, Hofmann D, Schulz A, Biehl M, Hammer B (2014)
Valid interpretation of feature relevance for linear data mappings.
In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Institute of Electrical & Electronics Engineers (IEEE): 149-156.
PUB | DOI
 
[270]
2014 | Book Chapter | PUB-ID: 2900324
Gisbrecht A, Schulz A, Hammer B (2014)
Discriminative Dimensionality Reduction for the Visualization of Classifiers.
In: Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing, 318. Springer Science + Business Media: 39-56.
PUB | DOI
 
[269]
2014 | Journal Article | PUB-ID: 2678214
Hofmann D, Schleif F-M, Paaßen B, Hammer B (2014)
Learning interpretable kernelized prototype-based models.
Neurocomputing 141: 84-96.
PUB | DOI | WoS
 
[268]
2014 | Journal Article | PUB-ID: 2734058
Gross S, Mokbel B, Paaßen B, Hammer B, Pinkwart N (2014)
Example-based feedback provision using structured solution spaces.
International Journal of Learning Technology 9(3): 248-280.
PUB | DOI
 
[267]
2014 | Conference Paper | PUB-ID: 2710067
Mokbel B, Paaßen B, Hammer B (2014)
Efficient Adaptation of Structure Metrics in Prototype-Based Classification.
In: Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa A (Eds); Lecture Notes in Computer Science, 8681. Springer: 571-578.
PUB | PDF | DOI
 
[266]
2014 | Conference Paper | PUB-ID: 2673554
Mokbel B, Paaßen B, Hammer B (2014)
Adaptive distance measures for sequential data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 265-270.
PUB | PDF
 
[265]
2014 | Journal Article | PUB-ID: 2672504
Zhu X, Schleif F-M, Hammer B (2014)
Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data.
Pattern Recognition Lettters 49: 138-145.
PUB | DOI | WoS
 
[264]
2014 | Journal Article | PUB-ID: 2615730
Hammer B, Hofmann D, Schleif F-M, Zhu X (2014)
Learning vector quantization for (dis-)similarities.
NeuroComputing 131: 43-51.
PUB | DOI | WoS
 
[263]
2014 | Conference Paper | PUB-ID: 2673559
Hammer B, He H, Martinetz T (2014)
Learning and modeling big data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 343-352.
PUB
 
[262]
2014 | Conference Paper | PUB-ID: 2673557
Schulz A, Gisbrecht A, Hammer B (2014)
Relevance learning for dimensionality reduction.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 165-170.
PUB
 
[261]
2014 | Conference Paper | PUB-ID: 2673545
Nebel D, Hammer B, Villmann T (2014)
Supervised Generative Models for Learning Dissimilarity Data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 35-40.
PUB
 
[260]
2013 | Conference Paper | PUB-ID: 2670686
Gross S, Mokbel B, Hammer B, Pinkwart N (2013)
Towards a Domain-Independent ITS Middleware Architecture.
In: 2013 IEEE 13th International Conference on Advanced Learning Technologies. 408-409.
PUB | DOI | WoS
 
[259]
2013 | Conference Paper | PUB-ID: 2625185
Mokbel B, Gross S, Paaßen B, Pinkwart N, Hammer B (2013)
Domain-Independent Proximity Measures in Intelligent Tutoring Systems.
In: Proceedings of the 6th International Conference on Educational Data Mining (EDM). D'Mello SK, Calvo RA, Olney A (Eds); 334-335.
PUB
 
[258]
2013 | Conference Paper | PUB-ID: 2909359
Nebel D, Hammer B, Villmann T (2013)
A Median Variant of Generalized Learning Vector Quantization.
In: ICONIP (2). 19-26.
PUB
 
[257]
2013 | Conference Paper | PUB-ID: 2909358
Strickert M, Hammer B, Villmann T, Biehl M (2013)
Regularization and Improved Interpretation of Linear Data Mappings and Adaptive Distance Measures.
In: IEEE SSCI CIDM 2013. IEEE Computational Intelligence Society: 10-17.
PUB
 
[256]
2013 | Conference Paper | PUB-ID: 2909357
Gisbrecht A, Hammer B, Mokbel B, Sczyrba A (2013)
Nonlinear dimensionality reduction for cluster identification in metagenomic samples.
In: IV. 174-179.
PUB
 
[255]
2013 | Conference Paper | PUB-ID: 2622456
Schulz A, Gisbrecht A, Hammer B (2013)
Using Nonlinear Dimensionality Reduction to Visualize Classifiers.
In: Advances in computational intelligence. Proceedings. Vol 1. Rojas I, Joya G, Gabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 59-68.
PUB | DOI | WoS
 
[254]
2013 | Journal Article | PUB-ID: 2612736
Mokbel B, Lueks W, Gisbrecht A, Hammer B (2013)
Visualizing the quality of dimensionality reduction.
Neurocomputing 112: 109-123.
PUB | DOI | WoS
 
[253]
2013 | Journal Article | PUB-ID: 2607146
Hammer B, Keim D, Lawrence N, Lebanon G (2013)
Preface: Intelligent interactive data visualization.
Data Mining and Knowledge Discovery 27(1): 1-3.
PUB | DOI | WoS
 
[252]
2013 | Conference Paper | PUB-ID: 2622454
Hammer B, Gisbrecht A, Schulz A (2013)
Applications of discriminative dimensionality reduction.
In: Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods. SCITEPRESS: 33-41.
PUB | DOI
 
[251]
2013 | Conference Paper | PUB-ID: 2622467
Schulz A, Gisbrecht A, Hammer B (2013)
Classifier inspection based on different discriminative dimensionality reductions.
In: Workshop NC^2 2013. TR Machine Learning Reports: 77-86.
PUB
 
[250]
2013 | Conference Paper | PUB-ID: 2625202
Schleif F-M, Zhu X, Hammer B (2013)
Sparse prototype representation by core sets.
In: IDEAL 2013. Hujun Yin et.al (Ed);.
PUB
 
[249]
2013 | Conference Paper | PUB-ID: 2625194
Gisbrecht A, Miche Y, Hammer B, Lendasse A (2013)
Visualizing Dependencies of Spectral Features using Mutual Information.
In: ESANN, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 573-578.
PUB
 
[248]
2013 | Conference Paper | PUB-ID: 2625199
Hofmann D, Hammer B (2013)
Sparse approximations for kernel learning vector quantization.
In: ESANN. .
PUB
 
[247]
2013 | Conference Paper | PUB-ID: 2625207
Gross S, Mokbel B, Hammer B, Pinkwart N (2013)
Towards Providing Feedback to Students in Absence of Formalized Domain Models.
In: AIED. 644-648.
PUB
 
[246]
2013 | Conference Paper | PUB-ID: 2615717
Zhu X, Schleif F-M, Hammer B (2013)
Secure Semi-supervised Vector Quantization for Dissimilarity Data.
In: IWANN (1). Rojas I, Joya G, Cabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 347-356.
PUB | DOI
 
[245]
2013 | Conference Paper | PUB-ID: 2615701
Zhu X, Schleif F-M, Hammer B (2013)
Semi-Supervised Vector Quantization for proximity data.
In: Proceedings of ESANN 2013. 89-94.
PUB
 
[244]
2012 | Journal Article | PUB-ID: 2625232
Gisbrecht A, Mokbel B, Schleif F-M, Zhu X, Hammer B (2012)
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(5).
PUB | DOI | WoS | PubMed | Europe PMC
 
[243]
2012 | Journal Article | PUB-ID: 2509858
Kaestner M, Hammer B, Biehl M, Villmann T (2012)
Functional relevance learning in generalized learning vector quantization.
Neurocomputing 90: 85-95.
PUB | DOI | WoS
 
[242]
2012 | Conference Paper | PUB-ID: 2909356
Mokbel B, Lueks W, Gisbrecht A, Biehl M, Hammer B (2012)
Visualizing the quality of dimensionality reduction.
In: ESANN 2012. Verleysen M (Ed); 179--184.
PUB
 
[241]
2012 | Journal Article | PUB-ID: 2671281
Hammer B, Villmann T (2012)
Special issue on new challenges in neural computation 2012.
Neurocomputing 131: 1-1.
PUB | DOI | WoS
 
[240]
2012 | Conference Paper | PUB-ID: 2622449
Schulz A, Gisbrecht A, Bunte K, Hammer B (2012)
How to visualize a classifier?
In: Proceedings of the Workshop - New Challenges in Neural Computation 2012. Machine Learning Reports: 73-83.
PUB
 
[239]
2012 | Conference Paper | PUB-ID: 2622453
Hammer B, Gisbrecht A, Schulz A (2012)
How to Visualize Large Data Sets?
Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile.
PUB | DOI
 
[238]
2012 | Conference Paper | PUB-ID: 2536426
Mokbel B, Gross S, Lux M, Pinkwart N, Hammer B (2012)
How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
In: Artificial Neural Networks in Pattern Recognition. 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Artificial Intelligence, 7477. Springer Berlin Heidelberg: 1-13.
PUB | PDF | DOI
 
[237]
2012 | Conference Paper | PUB-ID: 2671172
Hofmann D, Gisbrecht A, Hammer B (2012)
Discriminative probabilistic prototype based models in kernel space.
In: Workshop NC^2 2012. TR Machine Learning Reports.
PUB
 
[236]
2012 | Journal Article | PUB-ID: 2625223
Hammer B (2012)
Challenges in Neural Computation.
Künstliche Intelligenz : KI 26(4): 333-340.
PUB | DOI
 
[235]
2012 | Journal Article | PUB-ID: 2625225
Hammer B (2012)
Special Issue on Neural Learning Paradigms.
Künstliche Intelligenz :KI 26(4): 329-332.
PUB | DOI
 
[234]
2012 | Conference Paper | PUB-ID: 2625238
Hofmann D, Gisbrecht A, Hammer B (2012)
Efficient Approximations of Kernel Robust Soft LVQ.
In: WSOM. .
PUB
 
[233]
2012 | Conference Paper | PUB-ID: 2625265
Gisbrecht A, Sovilj D, Hammer B, Lendasse A (2012)
Relevance learning for time series inspection.
In: ESANN 2012. Verleysen M (Ed); 489-494.
PUB
 
[232]
2012 | Conference Paper | PUB-ID: 2625254
Hofmann D, Hammer B (2012)
Kernel Robust Soft Learning Vector Quantization.
In: Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Computer Science, 7477. Springer: 14-23.
PUB
 
[231]
2012 | Conference Paper | PUB-ID: 2625271
Bouveyron C, Hammer B, Villmann T (2012)
Recent developments in clustering algorithms.
In: ESANN 2012. Verleysen M (Ed); 447-458.
PUB
 
[230]
2012 | Conference Paper | PUB-ID: 2625247
Gisbrecht A, Hofmann D, Hammer B (2012)
Discriminative Dimensionality Reduction Mappings.
In: Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Hollmén J, Klawonn F, Tucker A (Eds); Lecture Notes in Computer Science, 7619. Springer: 126-138.
PUB
 
[229]
2012 | Conference Paper | PUB-ID: 2625276
Gisbrecht A, Mokbel B, Hammer B (2012)
Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction.
In: IJCNN. .
PUB
 
[228]
2012 | Conference Paper | PUB-ID: 2625242
Gross S, Mokbel B, Hammer B, Pinkwart N (2012)
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI. 27-38.
PUB
 
[227]
2012 | Conference Paper | PUB-ID: 2625260
Gisbrecht A, Lueks W, Mokbel B, Hammer B (2012)
Out-of-sample kernel extensions for nonparametric dimensionality reduction.
In: ESANN 2012. 531-536.
PUB
 
[226]
2012 | Conference Paper | PUB-ID: 2615750
Schleif F-M, Zhu X, Gisbrecht A, Hammer B (2012)
Fast approximated relational and kernel clustering.
In: Proceedings of ICPR 2012. IEEE: 1229-1232.
PUB
 
[225]
2012 | Conference Paper | PUB-ID: 2615756
Schleif F-M, Zhu X, Hammer B (2012)
Soft Competitive Learning for large data sets.
In: Proceedings of MCSD 2012. 141-151.
PUB | DOI
 
[224]
2012 | Conference Paper | PUB-ID: 2534877
Schleif F-M, Mokbel B, Gisbrecht A, Theunissen L, Dürr V, Hammer B (2012)
Learning Relevant Time Points for Time-Series Data in the Life Sciences.
In: ICANN (2). Lecture Notes in Computer Science, 7553. 531-539.
PUB | DOI
 
[223]
2012 | Conference Paper | PUB-ID: 2536437
Gross S, Zhu X, Hammer B, Pinkwart N (2012)
Cluster based feedback provision strategies in intelligent tutoring systems.
In: Proceedings of the 11th international conference on Intelligent Tutoring Systems. Berlin, Heidelberg: Springer-Verlag: 699-700.
PUB | PDF | DOI
 
[222]
2012 | Conference Paper | PUB-ID: 2536444
Gross S, Mokbel B, Hammer B, Pinkwart N (2012)
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI 2012: Die 10. e-Learning Fachtagung Informatik. Desel J, Haake JM, Spannagel C (Eds); Hagen, Germany: Köllen: 27-38.
PUB | PDF
 
[221]
2012 | Conference Paper | PUB-ID: 2534905
Schleif F-M, Gisbrecht A, Hammer B (2012)
Relevance learning for short high-dimensional time series in the life sciences.
In: IJCNN. 1-8.
PUB | DOI
 
[220]
2012 | Journal Article | PUB-ID: 2489405
Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2012)
Limited Rank Matrix Learning, discriminative dimension reduction and visualization.
Neural Networks 26: 159-173.
PUB | DOI | WoS | PubMed | Europe PMC
 
[219]
2012 | Journal Article | PUB-ID: 2509852
Zhu X, Gisbrecht A, Schleif F-M, Hammer B (2012)
Approximation techniques for clustering dissimilarity data.
Neurocomputing 90: 72-84.
PUB | DOI | WoS
 
[218]
2012 | Conference Paper | PUB-ID: 2534910
Zhu X, Schleif F-M, Hammer B (2012)
Patch Processing for Relational Learning Vector Quantization.
In: ISNN (1). 55-63.
PUB | DOI
 
[217]
2012 | Conference Paper | PUB-ID: 2534888
Schleif F-M, Zhu X, Hammer B (2012)
A Conformal Classifier for Dissimilarity Data.
In: AIAI (2). 234-243.
PUB | DOI
 
[216]
2012 | Conference Paper | PUB-ID: 2534868
Hammer B, Mokbel B, Schleif F-M, Zhu X (2012)
White Box Classification of Dissimilarity Data.
In: HAIS (1). 309-321.
PUB | DOI | WoS
 
[215]
2012 | Journal Article | PUB-ID: 2534839
Gisbrecht A, Mokbel B, Schleif F-M, Zhu X, Hammer B (2012)
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(05): 1250021.
PUB | DOI | WoS | PubMed | Europe PMC
 
[214]
2012 | Journal Article | PUB-ID: 2474292
Bunte K, Biehl M, Hammer B (2012)
A General Framework for Dimensionality-Reducing Data Visualization Mapping.
Neural Computation 24(3): 771-804.
PUB | DOI | WoS
 
[213]
2011 | Journal Article | PUB-ID: 2309980
Schleif F-M, Villmann T, Hammer B, Schneider P (2011)
Efficient Kernelized Prototype-based Classification.
International Journal of Neural Systems 21(06): 443-457.
PUB | DOI | WoS | PubMed | Europe PMC
 
[212]
2011 | Conference Paper | PUB-ID: 2276480
Gisbrecht A, Schleif F-M, Zhu X, Hammer B (2011)
Linear time heuristics for topographic mapping of dissimilarity data.
In: Intelligent Data Engineering and Automated Learning - IDEAL 2011: IDEAL 2011, 12th international conference, Norwich, UK, September 7 - 9, 2011 ; proceedings. Lecture Notes in Computer Science, 6936. Berlin, Heidelberg: Springer: 25-33.
PUB | DOI
 
[211]
2011 | Journal Article | PUB-ID: 2534994
Schleif F-M, Gisbrecht A, Hammer B (2011)
Supervised learning of short and high-dimensional temporal sequences for life science measurements.
CoRR 2011(1; abs/1110.2416.).
PUB
 
[210]
2011 | Journal Article | PUB-ID: 2276540
Gisbrecht A, Hammer B (2011)
Relevance learning in generative topographic mapping.
Neurocomputing 74(9): 1351-1358.
PUB | DOI | WoS
 
[209]
2011 | Journal Article | PUB-ID: 2276506
Bunte K, Hammer B, Villmann T, Biehl M, Wismueller A (2011)
Neighbor embedding XOM for dimension reduction and visualization.
Neurocomputing 74(9): 1340-1350.
PUB | DOI | WoS
 
[208]
2011 | Journal Article | PUB-ID: 2276531
Gisbrecht A, Mokbel B, Hammer B (2011)
Relational Generative Topographic Mapping.
Neurocomputing 74(9): 1359-1371.
PUB | DOI | WoS
 
[207]
2011 | Conference Paper | PUB-ID: 2091665
Zhu X, Hammer B (2011)
Patch Affinity Propagation.
Presented at the 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium.
PUB
 
[206]
2011 | Conference Paper | PUB-ID: 2276500
Kaestner M, Hammer B, Biehl M, Villmann T (2011)
Generalized Functional Relevance Learning Vector Quantization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 93-98.
PUB
 
[205]
2011 | Conference Paper | PUB-ID: 2276492
Schleif F-M, Gisbrecht A, Hammer B (2011)
Accelerating Kernel Neural Gas.
In: ICANN'2011. Kaski S, Honkela T, Gitolami M, Dutch W (Eds);.
PUB
 
[204]
2011 | Conference Paper | PUB-ID: 2276485
Hammer B, Gisbrecht A, Hasenfuss A, Mokbel B, Schleif F-M, Zhu X (2011)
Topographic Mapping of Dissimilarity Data.
In: WSOM'11. .
PUB
 
[203]
2011 | Conference Paper | PUB-ID: 2276527
Bunte K, Biehl M, Hammer B (2011)
Dimensionality Reduction Mappings.
In: IEEE Symposium on Computational Intelligence and Data Mining. pp. 349-356.
PUB | DOI
 
[202]
2011 | Conference Paper | PUB-ID: 2276522
Gisbrecht A, Hammer B, Schleif F-M, Zhu X (2011)
Accelerating dissimilarity clustering for biomedical data analysis.
In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp.154-161.
PUB
 
[201]
2011 | Conference Paper | PUB-ID: 2276517
Bunte K, Biehl M, Hammer B (2011)
Supervised dimension reduction mappings.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 281-286.
PUB
 
[200]
2011 | Conference Paper | PUB-ID: 2276512
Hammer B, Biehl M, Bunte K, Mokbel B (2011)
A general framework for dimensionality reduction for large data sets.
In: WSOM'11. .
PUB
 
[199]
2011 | Journal Article | PUB-ID: 1993288
Arnonkijpanich B, Hasenfuss A, Hammer B (2011)
Local matrix adaptation in topographic neural maps.
Neurocomputing 74(4): 522-539.
PUB | DOI | WoS
 
[198]
2010 | Conference Paper | PUB-ID: 1993536
Hammer B, Hasenfuss A (2010)
Clustering very large dissimilarity data sets.
In: Artificial Neural Networks in Pattern Recognition (ANNPR 2010). Proceedings. Schwenker F, El Gayar N (Eds); Lecture Notes in Artificial Intelligence, 5998. Berlin: Springer: 259-273.
PUB | DOI
 
[197]
2010 | Conference Paper | PUB-ID: 1796018
Arnonkijpanich B, Hasenfuss A, Hammer B (2010)
Local matrix learning in clustering and applications for manifold visualization.
Neural Networks 23(4): 476-486.
PUB | DOI | WoS | PubMed | Europe PMC
 
[196]
2010 | Conference Paper | PUB-ID: 1993978
Schleif F-M, Villmann T, Hammer B, Schneider P, Biehl M (2010)
Generalized derivative based Kernelized learning vector quantization.
In: Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Fyfe C, Tino P, Charles D, Garcia-Osorio C, Yin H (Eds); Berlin u.a.: Springer: 21-28.
PUB | DOI
 
[195]
2010 | Journal Article | PUB-ID: 1993466
Gori M, Hammer B, Hitzler P, Palm G (2010)
Perspectives and challenges for recurrent neural network training.
Logic Journal of the IGPL 18(5): 617-619.
PUB | DOI | WoS
 
[194]
2010 | Journal Article | PUB-ID: 1795962
Schneider P, Bunte K, Stiekema H, Hammer B, Villmann T, Biehl M (2010)
Regularization in Matrix Relevance Learning.
IEEE Transactions on Neural Networks 21(5): 831-840.
PUB | DOI | WoS | PubMed | Europe PMC
 
[193]
2010 | Conference Paper | PUB-ID: 2276547
Mokbel B, Gisbrecht A, Hammer B (2010)
On the effect of clustering on quality assessment measures for dimensionality reduction.
In: NIPS workshop on Challenges of Data Visualization. .
PUB
 
[192]
2010 | Conference Paper | PUB-ID: 2276543
Gisbrecht A, Mokbel B, Hammer B (2010)
The Nystrom approximation for relational generative topographic mappings.
In: NIPS workshop on challenges of Data Visualization. .
PUB
 
[191]
2010 | Conference (Editor) | PUB-ID: 2276535
Hammer B, Hitzler P, Maass W, Toussaint M (Eds) (2010)
Learning paradigms in dynamic environments, 25.07.10-30.07.20.; 10302.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
PUB
 
[190]
2010 | Journal Article | PUB-ID: 1794373
Hammer B, Hasenfuss A (2010)
Topographic Mapping of Large Dissimilarity Data Sets.
Neural Computation 22(9): 2229-2284.
PUB | DOI | WoS | PubMed | Europe PMC
 
[189]
2010 | Conference Paper | PUB-ID: 1993448
Gisbrecht A, Hammer B (2010)
Relevance learning in generative topographic maps.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 387-392.
PUB
 
[188]
2010 | Journal Article | PUB-ID: 1929672
Witoelar AW, Ghosh A, de Vries JJG, Hammer B, Biehl M (2010)
Window-Based Example Selection in Learning Vector Quantization.
Neural Computing 22(11): 2924-2961.
PUB | DOI | WoS | PubMed | Europe PMC
 
[187]
2010 | Conference Paper | PUB-ID: 1993452
Gisbrecht A, Mokbel B, Hammer B (2010)
Relational Generative Topographic Map.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 277-282.
PUB
 
[186]
2010 | Conference Paper | PUB-ID: 1993457
Gisbrecht A, Mokbel B, Hasenfuss A, Hammer B (2010)
Visualizing Dissimilarity Data using generative topographic mapping.
In: KI'2010. Dillmann R, Beyerer J, Hanebeck UD, Schulz T (Eds); 227-237.
PUB
 
[185]
2010 | Journal Article | PUB-ID: 1993435
Geweniger T, Zülke D, Hammer B, Villmann T (2010)
Median fuzzy-c-means for clustering dissimilarity data.
Neurocomputing 73(7-9): 1109-1116.
PUB | DOI | WoS
 
[184]
2010 | Journal Article | PUB-ID: 1796189
Bunte K, Hammer B, Wismueller A, Biehl M (2010)
Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data.
Neurocomputing 73(7-9): 1074-1092.
PUB | DOI | WoS
 
[183]
2010 | Conference Paper | PUB-ID: 1993367
Bunte K, Hammer B, Villmann T, Biehl M, Wismüller A (2010)
Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization.
In: ESANN'10. Proceedings of the 18th European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: D side: 87-92.
PUB
 
[182]
2010 | Conference Paper | PUB-ID: 1993273
Arnonkijpanich B, Hammer B (2010)
Global Coordination based on Matrix Neural Gas for Dynamic Texture Synthesis.
In: ANNPR'2010. Lecture Notes in Artificial Intelligence, 5998. El Gayar N, Schwenker F (Eds); Springer: 84-95.
PUB
 
[181]
2010 | Journal Article | PUB-ID: 1796195
Schneider P, Biehl M, Hammer B (2010)
Hyperparameter learning in probabilistic prototype-based models.
Neurocomputing 73(7-9): 1117-1124.
PUB | DOI | WoS
 
[180]
2010 | Conference Paper | PUB-ID: 1994127
Villmann T, Haase S, Schleif F-M, Hammer B (2010)
Divergence Based Online Learning in Vector Quantization.
In: Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Rutkowski L, Scherer R, Tadeusiewicz R, Zadeh L, Zurada J (Eds); Berlin, Heidelberg: Springer: 479-486.
PUB | DOI
 
[179]
2010 | Conference Paper | PUB-ID: 1994227
Villmann T, Schleif F-M, Hammer B (2010)
Sparse representation of data.
In: ESANN'10. Verleysen M (Ed); D side: 225-234.
PUB
 
[178]
2010 | Conference Paper | PUB-ID: 1994138
Villmann T, Haase S, Schleif F-M, Hammer B, Biehl M (2010)
The Mathematics of Divergence Based Online Learning in Vector Quanitzation.
In: ANNPR'2010. El Gayar N, Schwenker F (Eds); Berlin, Heidelberg: Springer: 108-119.
PUB
 
[177]
2010 | Journal Article | PUB-ID: 1994034
Simmuteit S, Schleif F-M, Villmann T, Hammer B (2010)
Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints.
Knowledge and Information Systems 25(2): 327-343.
PUB | DOI | WoS
 
[176]
2009 | Conference Paper | PUB-ID: 1993679
Hammer B, Schrauwen B, Steil JJ (2009)
Recent advances in efficient learning of recurrent networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brugge: d-facto: 213-226.
PUB
 
[175]
2009 | Conference Paper | PUB-ID: 1993361
Bunte K, Hammer B, Biehl M (2009)
Nonlinear dimension reduction and visualization of labeled data.
In: International Conference on Computer Analysis of Images and Patterns. Jiang X, Petkov N (Eds); Lecture Notes in Computer Science, 5702, Berlin: Springer: 1162-1170.
PUB | DOI
 
[174]
2009 | Conference Paper | PUB-ID: 1993422
Geweniger T, Zühlke D, Hammer B, Villmann T (2009)
Fuzzy variant of affinity propagation in comparison to median fuzzy c-means.
In: Advances in Self-Organizing Maps. Principe JC, Miikkulainen R (Eds); 72-79.
PUB | DOI
 
[173]
2009 | Journal Article | PUB-ID: 1993984
Schleif F-M, Villmann T, Kostrzewa M, Hammer B, Gammerman A (2009)
Cancer Informatics by Prototype-networks in Mass Spectrometry.
Artificial Intelligence in Medicine 45(2-3): 215-228.
PUB | DOI | WoS | PubMed | Europe PMC
 
[172]
2009 | Conference Paper | PUB-ID: 1993835
Mokbel B, Hasenfuss A, Hammer B (2009)
Graph-based Representation of Symbolic Musical Data.
In: Graph-Based Representation in Pattern Recognition (GbRPR 2009). Lecture Notes in Computer Science, 5534. Torsello A, Escolano F, Brun L (Eds); Berlin: Springer: 42-51.
PUB | DOI
 
[171]
2009 | Report | PUB-ID: 1993316
Biehl M, Hammer B, Schleif F-M, Schneider P, Villmann T (2009)
Stationarity of Matrix Relevance Learning Vector Quantization. Machine Learning Reports.
Leipzig: Universität Leipzig.
PUB
 
[170]
2009 | Book Chapter | PUB-ID: 1994160
Villmann T, Hammer B, Biehl M (2009)
Some theoretical aspects of the neural gas vector quantizer.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Springer: 23-34.
PUB | DOI
 
[169]
2009 | Conference (Editor) | PUB-ID: 1994310
Biehl M, Hammer B, Hochreiter S, Kremer SC, Villmann T (Eds) (2009)
Similarity-based learning on structures.; 9081.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
PUB
 
[168]
2009 | Book (Editor) | PUB-ID: 1994316
Biehl M, Hammer B, Verleysen M, Villmann T (Eds) (2009)
Similarity Based Clustering. Springer Lecture Notes Artificial Intelligence, 5400.
Berlin, Heidelberg: Springer.
PUB | DOI
 
[167]
2009 | Journal Article | PUB-ID: 1993269
Alex N, Hasenfuss A, Hammer B (2009)
Patch Clustering for Massive Data Sets.
Neurocomputing 72(7-9): 1455-1469.
PUB | DOI | WoS
 
[166]
2009 | Conference Paper | PUB-ID: 1993356
Bunte K, Biehl M, Hammer B (2009)
Nonlinear discriminative data visualization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 65-70.
PUB
 
[165]
2009 | Book Chapter | PUB-ID: 1993326
Biehl M, Hammer B, Schneider P, Villmann T (2009)
Metric learning for prototype based classification.
In: Innovations in Neural Information – Paradigms and Applications. Bianchini M, Maggini M, Scarselli F (Eds); Studies in Computational Intelligence, 247, Berlin: Springer: 183-199.
PUB | DOI
 
[164]
2009 | Conference Paper | PUB-ID: 1993429
Geweniger T, Zühlke D, Hammer B, Villmann T (2009)
Median variant of fuzzy-c-means.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 523-528.
PUB
 
[163]
2009 | Book Chapter | PUB-ID: 1993555
Hammer B, Hasenfuss A, Rossi F (2009)
Median topographic maps for biological data sets.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Springer: 92-117.
PUB | DOI
 
[162]
2009 | Conference Paper | PUB-ID: 1993994
Schneider P, Biehl M, Hammer B (2009)
Hyperparameter Learning in robust soft LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 517-522.
PUB
 
[161]
2009 | Conference Paper | PUB-ID: 1994152
Villmann T, Hammer B (2009)
Functional principal component learning using Oja's method and Sobolev norms.
In: Advances in Self-Organizing Maps. Principe JC, Miikkulainen R (Eds); 325-333.
PUB
 
[160]
2009 | Conference Paper | PUB-ID: 1994305
Witolaer A, Biehl M, Hammer B (2009)
Equilibrium properties of offline LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 535-540.
PUB
 
[159]
2009 | Journal Article | PUB-ID: 1994008
Schneider P, Biehl M, Hammer B (2009)
Distance learning in discriminative vector quantization.
Neural Computation 21(10): 2942-2969.
PUB | DOI | WoS | PubMed | Europe PMC
 
[158]
2009 | Journal Article | PUB-ID: 1994004
Schneider P, Biehl M, Hammer B (2009)
Adaptive relevance matrices in learning vector quantization.
Neural Computation 21(12): 3532-3561.
PUB | DOI | WoS | PubMed | Europe PMC
 
[157]
2008 | Conference Paper | PUB-ID: 1994072
Strickert M, Schneider P, Keilwagen J, Villmann T, Biehl M, Hammer B (2008)
Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics.
In: Artificial Neural Networks in Pattern Recognition. Third IAPR Workshop. Proceedings. Prevost L, Marinai S, Schwenker F (Eds); Lecture Notes in Computer Science, 5064, Berlin: Springer: 78-89.
PUB | DOI
 
[156]
2008 | Journal Article | PUB-ID: 1994253
Villmann T, Schleif F-M, Kostrzewa M, Walch A, Hammer B (2008)
Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods.
Briefings in Bioinformatics 9(2): 129-143.
PUB | DOI | WoS | PubMed | Europe PMC
 
[155]
2008 | Journal Article | PUB-ID: 2017617
Villmann T, Hammer B, Schleif F-M, Hermann W, Cottrell M (2008)
Fuzzy Classification Using Information Theoretic Learning Vector Quantization.
Neurocomputing 71(16-18): 3070-3076.
PUB | DOI | WoS
 
[154]
2008 | Report | PUB-ID: 1993278
Arnonkijpanich B, Hammer B, Hasenfuss A (2008)
Local Matrix Adaptation in Topographic Neural Maps. IfI-08-07.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[153]
2008 | Conference Paper | PUB-ID: 1994281
Winkler T, Drieseberg J, Hasenfuß A, Hammer B, Hormann K (2008)
Thinning Mesh Animations.
In: Proceedings of Vision, Modeling, and Visualization 2008. Deussen O, Keim D, Saupe D (Eds); Konstanz, Germany: Aka: 149-158.
PUB
 
[152]
2008 | Conference Paper | PUB-ID: 1993776
Hasenfuss A, Boerger W, Hammer B (2008)
Topographic processing of very large text datasets.
In: Smart Systems Engineering: Computational Intelligence in Architecting Systes (ANNIE 2008). Daglie CH (Ed); ASME Press: 525-532.
PUB | DOI
 
[151]
2008 | Conference Paper | PUB-ID: 1993788
Hasenfuss A, Hammer B (2008)
Single Pass Clustering and Classification of Large Dissimilarity Datasets.
In: Artificial Intelligence and Pattern Recognition. Prasad B, Sinha P, Ram A, Kerre EE (Eds); ISRST: 219-223.
PUB
 
[150]
2008 | Conference Paper | PUB-ID: 1994089
Strickert M, Sreenivasulu N, Villmann T, Hammer B (2008)
Robust Centroid-Based Clustering using Derivatives of Pearson Correlation.
In: BIOSIGNALS (2). Encarnação P, Veloso A (Eds); INSTICC - Institute for Systems and Technologies of Information, Control and Communication: 197-203.
PUB
 
[149]
2008 | Conference (Editor) | PUB-ID: 1994329
de Raedt L, Hammer B, Hitzler P, Maass W (Eds) (2008)
Recurrent Neural Networks - Models, Capacities, and Applications.; 8041.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
PUB
 
[148]
2008 | Journal Article | PUB-ID: 1993966
Schleif F-M, Villmann T, Hammer B (2008)
Prototype based Fuzzy Classification in Clinical Proteomics.
International Journal of Approximate Reasoning 47(1): 4-16.
PUB | DOI | WoS
 
[147]
2008 | Book Chapter | PUB-ID: 1993939
Schleif F-M, Villmann T, Hammer B (2008)
Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics.
In: Encyclopedia of Artificial Intelligence. Dopico JR-n R-al, Dorado J, Pazos A (Eds); IGI Global: 1337-1342.
PUB
 
[146]
2008 | Conference Paper | PUB-ID: 1993804
Hasenfuss A, Hammer B, Rossi F (2008)
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets.
In: Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064. Prevost L, Marinai S, Schwenker F (Eds); Berlin: Springer: 1-12.
PUB | DOI
 
[145]
2008 | Conference Paper | PUB-ID: 1993261
Alex N, Hammer B (2008)
Parallelizing single pass patch clustering.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere, Belgium: d-side publications: 227-232.
PUB
 
[144]
2008 | Conference Paper | PUB-ID: 1993282
Arnonkijpanich B, Hammer B, Hasenfuss A, Lursinsap C (2008)
Matrix Learning for Topographic Neural Maps.
In: ICANN (1). Lecture Notes in Computer Science, 5163. Kurková V, Neruda R, Koutn'ık J (Eds); Berlin: Springer: 572-582.
PUB
 
[143]
2008 | Report | PUB-ID: 1994012
Schneider P, Biehl M, Hammer B (2008)
Matrix Adaptation in Discriminative Vector Quantization. IfI Technical Report Seriess.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[142]
2008 | Conference Paper | PUB-ID: 1993798
Hasenfuss A, Hammer B, Geweniger T, Villmann T (2008)
Magnification Control in Relational Neural Gas.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 325-330.
PUB
 
[141]
2008 | Journal Article | PUB-ID: 1994290
Witoelar A, Biehl M, Ghosh A, Hammer B (2008)
Learning dynamics and robustness of vector quantization and neural gas.
Neurocomputing 71(7-9): 1210-1219.
PUB | DOI | WoS
 
[140]
2008 | Report | PUB-ID: 1993379
Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2008)
Discriminative Visualization by Limited Rank Matrix Learning. Machine Learning Reports.
Leipzig: Universität Leipzig.
PUB
 
[139]
2008 | Conference Paper | PUB-ID: 2001836
Geweniger T, Schleif F-M, Hasenfuss A, Hammer B, Villmann T (2008)
Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity.
In: ICONIP 2008. Köppen M, Kasabov NK, Coghill GG (Eds); Berlin, Heidelberg: Springer: 61-69.
PUB | DOI
 
[138]
2008 | Book Chapter | PUB-ID: 1993900
Schleif F-M, Hammer B, Villmann T (2008)
Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers.
In: Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Van de Werff M, Delder A, Tollenaar R (Eds); Berlin: Springer: 141-167.
PUB | DOI
 
[137]
2007 | Book Chapter | PUB-ID: 1994102
Tino P, Hammer B, Boden M (2007)
Markovian Bias of Neural-based Architectures With Feedback Connections.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 95-134.
PUB | DOI
 
[136]
2007 | Book Chapter | PUB-ID: 1993630
Hammer B, Micheli A, Sperduti A (2007)
Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 67-94.
PUB | DOI
 
[135]
2007 | Conference Paper | PUB-ID: 1993563
Hammer B, Hasenfuss A, Rossi F, Strickert M (2007)
Topographic Processing of Relational Data.
In: Proceedings of 6th International Workshop on Self-Organizing Maps. .
PUB
 
[134]
2007 | Conference Paper | PUB-ID: 1993907
Schleif F-M, Hammer B, Villmann T (2007)
Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg: Springer: 1036-1044.
PUB | DOI
 
[133]
2007 | Conference Paper | PUB-ID: 1993265
Alex N, Hammer B, Klawonn F (2007)
Single pass clustering for large data sets.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). .
PUB
 
[132]
2007 | Conference (Editor) | PUB-ID: 1994321
Biehl M, Hammer B, Verleysen M, Villmann T (Eds) (2007)
Similarity-based Clustering and its Application to Medicine and Biology.; 7131.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
PUB
 
[131]
2007 | Conference Paper | PUB-ID: 1993999
Schneider P, Biehl M, Hammer B (2007)
Relevance matrices in LVQ.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 37-42.
PUB
 
[130]
2007 | Conference Paper | PUB-ID: 1993782
Hasenfuss A, Hammer B (2007)
Relational topographic maps.
In: Advances in Intelligent Data Analysis VII, Proceedings of the 7th International Symposium on Intelligent Data Analysis. Berthold MR, Shawe-Taylor J, Lavrac N (Eds);4723. Berlin: Springer: 93-105.
PUB | DOI
 
[129]
2007 | Report | PUB-ID: 1993533
Hammer B, Hasenfuss A (2007)
Relational topographic Maps. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[128]
2007 | Conference Paper | PUB-ID: 1993541
Hammer B, Hasenfuss A (2007)
Relational Neural Gas.
In: KI 2007: Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence, 4667. Hertzberg J, Beetz M, Englert R (Eds); Berlin: Springer: 190-204.
PUB | DOI
 
[127]
2007 | Book (Editor) | PUB-ID: 1994326
Hammer B, Hitzler P (Eds) (2007)
Perspectives of Neural-Symbolic Integration. Studies in Computational Intelligence, 77.
Berlin: Springer.
PUB | DOI
 
[126]
2007 | Conference Paper | PUB-ID: 1994299
Witolaer A, Biehl M, Ghosh A, Hammer B (2007)
On the dynamics of vector quantization and neural gas.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 127-132.
PUB
 
[125]
2007 | Report | PUB-ID: 1993831
Melato M, Hammer B, Hormann K (2007)
Neural Gas for Surface Reconstruction. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[124]
2007 | Conference Paper | PUB-ID: 1993820
Hasenfuss A, Hammer B, Schleif F-M, Villmann T (2007)
Neural gas clustering for sparse proximity data.
In: Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg, Germany: Springer: 539-546.
PUB
 
[123]
2007 | Conference Paper | PUB-ID: 1993811
Hasenfuss A, Hammer B, Schleif F-M, Villmann T (2007)
Neural gas clustering for dissimilarity data with continuous prototypes.
In: Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 539-546.
PUB | DOI
 
[122]
2007 | Journal Article | PUB-ID: 1993911
Schleif F-M, Hammer B, Villmann T (2007)
Margin based Active Learning for LVQ Networks.
Neurocomputing 70(7-9): 1215-1224.
PUB | DOI | WoS
 
[121]
2007 | Journal Article | PUB-ID: 1993616
Hammer B, Hasenfuss A, Villmann T (2007)
Magnification control for batch neural gas.
Neurocomputing 70(7-9): 1225-1234.
PUB | DOI | WoS
 
[120]
2007 | Conference Paper | PUB-ID: 1994295
Witoelar A, Biehl M, Hammer B (2007)
Learning Vector Quantization: generalization ability and dynamics of competing prototypes.
In: Proceedings of 6th International Workshop on Self-Organizing Maps. .
PUB
 
[119]
2007 | Conference Paper | PUB-ID: 1993547
Hammer B, Hasenfuss A, Schleif F-M, Villmann T, Strickert M, Seiffert U (2007)
Intuitive Clustering of Biological Data.
In: Proceedings of International Joint Conference on Neural Networks. IEEE: 1877-1882.
PUB | DOI
 
[118]
2007 | Report | PUB-ID: 1993334
Blazewicz J, Ecker K, Hammer B (2007)
ICOLE-2007, German-Polish Workshop on Computational Biology, Scheduling and Machine Learning. Lessach, Austria, 27.05.-02.06.2007.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[117]
2007 | Conference Paper | PUB-ID: 1993746
Hammer B, Villmann T (2007)
How to process uncertainty in machine learning.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 79-90.
PUB
 
[116]
2007 | Conference Paper | PUB-ID: 1994258
Villmann T, Schleif F-M, Merenyi E, Hammer B (2007)
Fuzzy Labeled Self Organizing Map for Clasification of Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 556-563.
PUB | DOI
 
[115]
2007 | Journal Article | PUB-ID: 1993297
Biehl M, Ghosh A, Hammer B (2007)
Dynamics and generalization ability of LVQ algorithms.
Journal of Machine Learning Research 8: 323-360.
PUB
 
[114]
2007 | Conference Paper | PUB-ID: 1994267
Villmann T, Schleif F-M, Merenyi E, Strickert M, Hammer B (2007)
Class imaging of hyperspectral satellite remote sensing data using FLSOM.
In: Proceedings of 6th International Workshop on Self-Organizing Maps. .
PUB
 
[113]
2007 | Conference Paper | PUB-ID: 1993970
Schleif F-M, Villmann T, Hammer B (2007)
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps.
In: Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Masulli F, Mitra S, Pasi G (Eds); Berlin, Heidelberg: Springer: 563-570.
PUB | DOI
 
[112]
2007 | Report | PUB-ID: 1993922
Schleif F-M, Hasenfuss A, Hammer B (2007)
Aggregation of multiple peak lists by use of an improved neural gas network.
Leipzig: Universität Leipzig.
PUB
 
[111]
2007 | Conference Paper | PUB-ID: 1994016
Schneider P, Biehl M, Schleif F-M, Hammer B (2007)
Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data.
In: Proceedings of 6th International Workshop on Self-Organizing Maps. .
PUB
 
[110]
2007 | Conference Paper | PUB-ID: 1993848
Rossi F, Hasenfuß A, Hammer B (2007)
Accelerating Relational Clustering Algorithms With Sparse Prototype Representation.
In: Proceedings of 6th International Workshop on Self-Organizing Maps. .
PUB
 
[109]
2006 | Conference Paper | PUB-ID: 1994184
Villmann T, Hammer B, Schleif F-M, Geweniger T, Fischer T, Cottrell M (2006)
Prototype based classification using information theoretic learning.
In: Neural Information Processing, 13th International Conference. Proceedings. King I, Wang J, Chan L, Wang DLL (Eds); Lecture Notes in Computer Science, 4233, Part II. Berlin: Springer: 40-49.
PUB
 
[108]
2006 | Conference Paper | PUB-ID: 1993594
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median clustering.
In: Smart systems engineering : infra-structure systems engineering, bio-informatics and computational biology and evolutionary computation : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2006). Dagli CH (Ed); ASME Press series on intelligent engineering systems through artificial neural networks, 16, New York, NY: ASME Press: 623-632.
PUB
 
[107]
2006 | Journal Article | PUB-ID: 1993391
Cottrell M, Hammer B, Hasenfuss A, Villmann T (2006)
Batch and Median Neural Gas.
Neural Networks 19(6-7): 762-771.
PUB | DOI | WoS | PubMed | Europe PMC
 
[106]
2006 | Journal Article | PUB-ID: 1994237
Villmann T, Schleif F-M, Hammer B (2006)
Comparison of relevance learning vector quantization with other metric adaptive classification methods.
Neural Networks 19(5): 610-622.
PUB | DOI | WoS | PubMed | Europe PMC
 
[105]
2006 | Conference Paper | PUB-ID: 2017225
Hammer B, Villmann T, Schleif F-M, Albani C, Hermann W (2006)
Learning vector quantization classification with local relevance determination for medical data.
In: Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Rutkowski L, Tadeusiewicz R, Zadeh LA, Zurada J (Eds); Berlin, Heidelberg: Springer: 603-612.
PUB | DOI
 
[104]
2006 | Conference Paper | PUB-ID: 1993568
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median neural gas.
In: Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. Dagli C, Buczak A, Enke D, Embrechts A, Ersoy O (Eds); ASME Press: 623-633.
PUB
 
[103]
2006 | Report | PUB-ID: 1993584
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median clustering. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[102]
2006 | Conference Paper | PUB-ID: 1993578
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised Batch Neural Gas.
In: Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR). Schwenker F (Ed); Berlin: Springer Verlag: 33-45.
PUB | DOI
 
[101]
2006 | Journal Article | PUB-ID: 1994241
Villmann T, Schleif F-M, Hammer B (2006)
Prototype-based fuzzy classification with local relevance for proteomics.
Neurocomputing 69(16-18): 2425-2428.
PUB | DOI | WoS
 
[100]
2006 | Conference Paper | PUB-ID: 1994201
Villmann T, Hammer B, Seiffert U (2006)
Perspectives of Self-adapted Self-organizing Clustering in Organic Computing.
In: Biologically Inspired Approaches to Advanced Information Technology, Second International Workshop. Proceedings. Lecture Notes in Computer Science, 3853. Ijspeert AJ, Masuzawa T, Kusumoto S (Eds); Berlin: Springer: 141-159.
PUB | DOI
 
[99]
2006 | Journal Article | PUB-ID: 1993440
Ghosh A, Biehl M, Hammer B (2006)
Performance analysis of LVQ algorithms: a statistical physics approach.
Neural Networks 19(6-7): 817-829.
PUB | DOI | WoS | PubMed | Europe PMC
 
[98]
2006 | Conference Paper | PUB-ID: 1993659
Hammer B, Neubauer N (2006)
On the capacity of unsupervised recursive neural networks for symbol processing.
In: Workshop proceedings of NeSy'06. d'Avila Garcez A, Hitzler P, Tamburrini G (Eds);.
PUB
 
[97]
2006 | Conference Paper | PUB-ID: 1994028
Seiffert U, Hammer B, Kaski S, Villmann T (2006)
Neural Networks and Machine Learning in Bioinformatics - Theory and Applications.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 521-532.
PUB
 
[96]
2006 | Report | PUB-ID: 1993322
Biehl M, Hammer B, Schneider P (2006)
Matrix Learning in Learning Vector Quantization.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[95]
2006 | Conference Paper | PUB-ID: 1993895
Schleif F-M, Hammer B, Villmann T (2006)
Margin based Active Learning for LVQ Networks.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 539-544.
PUB
 
[94]
2006 | Conference Paper | PUB-ID: 1993611
Hammer B, Hasenfuss A, Villmann T (2006)
Magnification Control for Batch Neural Gas.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 7-12.
PUB
 
[93]
2006 | Conference Paper | PUB-ID: 1993889
Schleif F-M, Elssner T, Kostrzewa M, Villmann T, Hammer B (2006)
Machine Learning and Soft-Computing in Bioinformatics. A Short Journey.
In: Proc. of FLINS 2006. World Scientific Press: 541-548.
PUB
 
[92]
2006 | Journal Article | PUB-ID: 1993301
Biehl M, Ghosh A, Hammer B (2006)
Learning vector quantization: The dynamics of winner-takes-all algorithms.
Neurocomputing 69(7-9): 660-670.
PUB | DOI | WoS
 
[91]
2006 | Journal Article | PUB-ID: 1994082
Strickert M, Seiffert U, Sreenivasulu N, Weschke W, Villmann T, Hammer B (2006)
Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis.
Neurocomputing 69(7-9): 651-659.
PUB | DOI | WoS
 
[90]
2006 | Conference Paper | PUB-ID: 1994273
Villmann T, Seiffert U, Schleif F-M, Brüß C, Geweniger T, Hammer B (2006)
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes.
In: Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Schwenker F (Ed); Berlin: Springer: 46-56.
PUB | DOI
 
[89]
2006 | Journal Article | PUB-ID: 1994195
Villmann T, Hammer B, Schleif F-M, Geweniger T, Herrmann W (2006)
Fuzzy Classification by Fuzzy Labeled Neural Gas.
Neural Networks 19(6-7): 772-779.
PUB | DOI | WoS | PubMed | Europe PMC
 
[88]
2006 | Journal Article | PUB-ID: 1993762
Hammer B, Villmann T (2006)
Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern.
Künstliche Intelligenz 3(6): 5-11.
PUB
 
[87]
2006 | Conference Paper | PUB-ID: 1993878
Schleif F-M, Elssner T, Kostrzewa M, Villmann T, Hammer B (2006)
Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps.
In: 19th IEEE International Symposium on Computer- based Medical Systems. Lee DJ, Nutter B, Antani S, Mitra S, Archibald J (Eds); Los Alamitos: IEEE Computer Society Press: 919-924.
PUB | DOI
 
[86]
2005 | Journal Article | PUB-ID: 1994063
Strickert M, Hammer B, Blohm S (2005)
Unsupervised recursive sequences processing.
Neurocomputing 63: 69-97.
PUB | DOI | WoS
 
[85]
2005 | Journal Article | PUB-ID: 1993641
Hammer B, Micheli A, Sperduti A (2005)
Universal approximation capability of cascade correlation for structures.
Neural Computation 17(5): 1109-1159.
PUB | DOI | WoS
 
[84]
2005 | Conference Paper | PUB-ID: 1993305
Biehl M, Gosh A, Hammer B (2005)
The dynamics of Learning Vector Quantization.
In: ESANN'05. Verleysen M (Ed); Evere: d-side publishing: 13-18.
PUB
 
[83]
2005 | Journal Article | PUB-ID: 1993721
Hammer B, Strickert M, Villmann T (2005)
Supervised neural gas with general similarity measure.
Neural Processing Letters 21(1): 21-44.
PUB | DOI | WoS
 
[82]
2005 | Journal Article | PUB-ID: 1993671
Hammer B, Saunders C, Sperduti A (2005)
Special issue on neural networks and kernel methods for structured domains.
Neural Networks 18(8): 1015-1018.
PUB | DOI | WoS | PubMed | Europe PMC
 
[81]
2005 | Conference Paper | PUB-ID: 1993624
Hammer B, Micheli A, Neubauer N, Sperduti A, Strickert M (2005)
Self Organizing Maps for Time Series.
In: Proceedings of WSOM 2005. 115-122.
PUB
 
[80]
2005 | Conference Paper | PUB-ID: 1993665
Hammer B, Rechtien A, Strickert M, Villmann V (2005)
Relevance learning for mental disease classification.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 139-144.
PUB
 
[79]
2005 | Conference Paper | PUB-ID: 1994118
Tluk von Toschanowitz K, Hammer B, Ritter H (2005)
Relevance determination in reinforcement learning.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 369-374.
PUB
 
[78]
2005 | Book Chapter | PUB-ID: 1993710
Hammer B, Strickert M, Villmann T (2005)
Prototype based recognition of splice sites.
In: Bioinformatics using computational intelligence paradigms. Seiffert U, Jain LC, Schweitzer P (Eds); Berlin: Springer: 25-55.
PUB
 
[77]
2005 | Report | PUB-ID: 1993675
Hammer B, Schleif F-M, Villmann T (2005)
On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[76]
2005 | Journal Article | PUB-ID: 1993717
Hammer B, Strickert M, Villmann T (2005)
On the generalization ability of GRLVQ networks.
Neural Processing Letters 21(2): 109-120.
PUB | DOI | WoS
 
[75]
2005 | Journal Article | PUB-ID: 1993406
DasGupta B, Hammer B (2005)
On approximate learning by multi-layered feedforward circuits.
Theoretical Computer Science 348(1): 95-127.
PUB | DOI | WoS
 
[74]
2005 | Journal Article | PUB-ID: 1993396
Cottrell M, Hammer B, Villmann T (2005)
New Aspects in Neurocomputing.
Neurocomputing 63: 1-3.
PUB | DOI | WoS
 
[73]
2005 | Journal Article | PUB-ID: 1994057
Strickert M, Hammer B (2005)
Merge SOM for temporal data.
Neurocomputing 64: 39-71.
PUB | DOI | WoS
 
[72]
2005 | Conference Paper | PUB-ID: 1993974
Schleif F-M, Villmann T, Hammer B (2005)
Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data.
In: Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Bloch I, Petrosino A, Tettamanzi AGB (Eds); Berlin, Heidelberg: Springer: 290-296.
PUB | DOI
 
[71]
2005 | Journal Article | PUB-ID: 1993416
Gersmann K, Hammer B (2005)
Improving iterative repair strategies for scheduling with the SVM.
Neurocomputing 63: 271-292.
PUB | DOI | WoS
 
[70]
2005 | Conference Paper | PUB-ID: 1994249
Villmann T, Schleif F-M, Hammer B (2005)
Fuzzy labeled soft nearest neighbor classification with relevance learning.
In: Proceedings of the International Conference of Machine Learning Applications. Wani MA, Cios KJ, Hafeez K (Eds); Los Angeles: IEEE Press: 11-15.
PUB
 
[69]
2005 | Conference Paper | PUB-ID: 1994172
Villmann T, Hammer B, Schleif F-M, Geweniger T (2005)
Fuzzy Labeled Neural GAS for Fuzzy Classification.
In: Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Cottrell M (Ed); Paris, France: University Paris-1-Pantheon-Sorbonne: 283-290.
PUB
 
[68]
2005 | Conference Paper | PUB-ID: 1994219
Villmann T, Schleif F-M, Hammer B (2005)
Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization.
In: International Workshop on Integrative Bioinformatics. .
PUB
 
[67]
2005 | Conference Paper | PUB-ID: 1993444
Ghosh A, Biehl M, Hammer B (2005)
Dynamical Analysis of LVQ type learning rules.
In: Proceedings of WSOM. 578-594.
PUB
 
[66]
2005 | Conference Paper | PUB-ID: 1993750
Hammer B, Villmann T (2005)
Classification using non standard metrics.
In: ESANN'05. Verleysen M (Ed); Brussels: d-side publishing: 303-316.
PUB
 
[65]
2005 | Conference Paper | PUB-ID: 1993386
Cottrell M, Hammer B, Hasenfuss A, Villmann T (2005)
Batch NG.
In: Proceedings of WSOM 2005. 275-282.
PUB
 
[64]
2004 | Conference Paper | PUB-ID: 1993870
Schleif F-M, Clauss U, Villmann T, Hammer B (2004)
Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data.
In: Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Wani MA, Cios KJ, Hafeez K (Eds); Los Alamitos, CA, USA: IEEE Press: 374-379.
PUB
 
[63]
2004 | Conference Paper | PUB-ID: 1994049
Strickert M, Hammer B (2004)
Self-organizing context learning.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-side publications: 39-44.
PUB
 
[62]
2004 | Conference Paper | PUB-ID: 1993702
Hammer B, Strickert M, Villmann T (2004)
Relevance LVQ versus SVM.
In: Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070. Rutkowski L, Siekmann J, Tadeusiewicz R, Zadeh LA (Eds); Berlin: Springer: 592-597.
PUB
 
[61]
2004 | Journal Article | PUB-ID: 1993649
Hammer B, Micheli A, Sperduti A, Strickert M (2004)
Recursive self-organizing network models.
Neural Networks 17(8-9): 1061-1085.
PUB | DOI | WoS | PubMed | Europe PMC
 
[60]
2004 | Conference Paper | PUB-ID: 1994099
Tino P, Hammer B (2004)
On early stages of learning in connectionist models with feedback connections.
In: Compositional Connectionism in Cognitive Science. .
PUB
 
[59]
2004 | Conference Paper | PUB-ID: 1993620
Hammer B, Jain BJ (2004)
Neural methods for non-standard data.
In: European Symposium on Artificial Neural Networks'2004. Verleysen M (Ed); D-side publications: 281-292.
PUB
 
[58]
2004 | Conference Paper | PUB-ID: 1994168
Villmann T, Hammer B, Schleif F-M (2004)
Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection.
In: Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 592-597.
PUB
 
[57]
2004 | Conference Paper | PUB-ID: 1994212
Villmann T, Schleif F-M, Hammer B (2004)
Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag.
PUB
 
[56]
2004 | Conference Paper | PUB-ID: 1994111
Tluk von Toschanowitz K, Hammer B, Ritter H (2004)
Mapping the Design Space of Reinforcement Learning Problems - a Case Study.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Gross H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 251-261.
PUB
 
[55]
2004 | Conference Paper | PUB-ID: 1993419
Gersmann K, Hammer B (2004)
A reinforcement learning algorithm to improve scheduling search heuristics with the SVM.
In: IJCNN. .
PUB
 
[54]
2004 | Report | PUB-ID: 1993732
Hammer B, Tino P, Micheli A (2004)
A mathematical characterization of the architectural bias of recursive models. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[53]
2004 | Journal Article | PUB-ID: 1993654
Hammer B, Micheli A, Sperduti A, Strickert M (2004)
A general framework for unsupervised processing of structured data.
Neurocomputing 57: 3-35.
PUB | DOI | WoS
 
[52]
2003 | Journal Article | PUB-ID: 1994208
Villmann T, Merényi E, Hammer B (2003)
Neural maps in remote sensing image analysis.
Neural Networks 16(3-4): 389-403.
PUB
 
[51]
2003 | Conference Paper | PUB-ID: 1994053
Strickert M, Hammer B (2003)
Unsupervised recursive sequence processing.
In: 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); D-side publication: 27-32.
PUB
 
[50]
2003 | Conference Paper | PUB-ID: 1994223
Villmann T, Schleif F-M, Hammer B (2003)
Supervised Neural Gas and Relevance Learning in Learning Vector Quantization.
In: Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Yamakawa T (Ed); Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology: 47-52.
PUB
 
[49]
2003 | Journal Article | PUB-ID: 1993736
Hammer B, Tiño P (2003)
Recurrent Neural Networks with Small Weights Implement Definite Memory Machines.
Neural Computation 15(8): 1897-1929.
PUB
 
[48]
2003 | Book Chapter | PUB-ID: 1993487
Hammer B (2003)
Perspectives on learning symbolic data with connectionistic systems.
In: Adaptivity and Learning. Kühn R, Menzel R, Menzel W, Ratsch U, Richter MM, Stamatescu I (Eds); Berlin: Springer: 141-160.
PUB
 
[47]
2003 | Report | PUB-ID: 1993725
Hammer B, Strickert M, Villmann T (2003)
On the generalization ability of GRLVQ. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[46]
2003 | Conference Paper | PUB-ID: 1994060
Strickert M, Hammer B (2003)
Neural Gas for Sequences.
In: WSOM'03. 53-57.
PUB
 
[45]
2003 | Conference Paper | PUB-ID: 1993338
Bojer T, Hammer B, Koeers C (2003)
Monitoring technical systems with prototype based clustering.
In: ESANN 2003, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); Evere: D-side publication: 433-439.
PUB
 
[44]
2003 | Report | PUB-ID: 1994157
Villmann T, Hammer B (2003)
Metric adaptation and relevance learning in learning vector quantization. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[43]
2003 | Conference Paper | PUB-ID: 1993754
Hammer B, Villmann T (2003)
Mathematical Aspects of Neural Networks.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2003). Verleysen M (Ed); Brussels, Belgium: d-side: 59-72.
PUB
 
[42]
2003 | Conference Paper | PUB-ID: 1993412
Gersmann K, Hammer B (2003)
Improving iterative repair strategies for scheduling with the SVM.
In: ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); Evere: D-side publication: 235-240.
PUB
 
[41]
2003 | Conference Paper | PUB-ID: 1993349
Bojer T, Hammer B, Strickert M, Villmann T (2003)
Determining Relevant Input Dimensions for the Self-Organizing Map.
In: Neural Networks and Soft Computing (Proc. ICNNSC 2002). Rutkowski L, Kacprzyk J (Eds); Physica-Verlag: 388-393.
PUB
 
[40]
2003 | Journal Article | PUB-ID: 1994108
Tiño P, Hammer B (2003)
Architectural Bias in Recurrent Neural Networks: Fractal Analysis.
Neural Computation 15(8): 1931-1957.
PUB
 
[39]
2003 | Journal Article | PUB-ID: 1993530
Hammer B, Gersmann K (2003)
A Note on the Universal Approximation Capability of Support Vector Machines.
Neural Processing Letters 17(1): 43-53.
PUB
 
[38]
2003 | Report | PUB-ID: 1993645
Hammer B, Micheli a., Sperduti A (2003)
A general framework for self-organizing structure processing neural networks.
Pisa: Universita di Pisa, Dipartimento die Informatica.
PUB
 
[37]
2002 | Conference Paper | PUB-ID: 1993688
Hammer B, Steil JJ (2002)
Perspectives on Learning with Recurrent Neural Networks.
In: Proc. European Symposium Artificial Neural Networks. Verleysen M (Ed); D-side publication: 357-368.
PUB
 
[36]
2002 | Journal Article | PUB-ID: 1993765
Hammer B, Villmann T (2002)
Generalized Relevance Learning Vector Quantization.
Neural Networks 15(8-9): 1059-1068.
PUB
 
[35]
2002 | Conference Paper | PUB-ID: 1994146
Villmann T, Hammer B (2002)
Supervised Neural Gas for Learning Vector Quantization.
In: Proc. of the 5th German Workshop on Artificial Life. Polani D, Kim J, Martinetz T (Eds); Berlin: Akademische Verlagsgesellschaft - infix - IOS Press: 9-16.
PUB
 
[34]
2002 | Conference Paper | PUB-ID: 1993697
Hammer B, Strickert M, Villmann T (2002)
Rule Extraction from Self-Organizing Networks.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 877-883.
PUB
 
[33]
2002 | Journal Article | PUB-ID: 1993508
Hammer B (2002)
Recurrent neural networks for structured data – a unifying approach and its properties.
Cognitive Systems Research 3(2): 145-165.
PUB
 
[32]
2002 | Report | PUB-ID: 1993729
Hammer B, Tino P (2002)
Neural networks with small weights implement finite memory machines. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[31]
2002 | Conference Paper | PUB-ID: 1993692
Hammer B, Strickert M, Villmann T (2002)
Learning Vector Quantization for Multimodal Data.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 370-376.
PUB
 
[30]
2002 | Book Chapter | PUB-ID: 1993471
Hammer B (2002)
Compositionality in Neural Systems.
In: Handbook of Brain Theory and Neural Networks. Arbib M (Ed); 2nd. MIT Press: 244-248.
PUB
 
[29]
2002 | Conference Paper | PUB-ID: 1993758
Hammer B, Villmann T (2002)
Batch-GRLVQ.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2002). Verleysen M (Ed); Brussels, Belgium: d-side: 295-300.
PUB
 
[28]
2002 | Conference Paper | PUB-ID: 1994095
Tino P, Hammer B (2002)
Architectural bias in recurrent neural networks – fractal analysis.
In: Proc. International Conf. on Artificial Neural Networks. Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer: 370-376.
PUB
 
[27]
2002 | Conference Paper | PUB-ID: 1993636
Hammer B, Micheli A, Sperduti A (2002)
A general framework for unsupervised processing of structured data.
In: ESANN 2002, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); De-side publication: 389-394.
PUB
 
[26]
2001 | Conference Paper | PUB-ID: 1993343
Bojer T, Hammer B, Schunk D, Tluk von Toschanowitz K (2001)
Relevance determination in learning vector quantization.
In: ESANN'2001. Verleysen M (Ed); D-facto publications: 271-276.
PUB
 
[25]
2001 | Conference Paper | PUB-ID: 1993474
Hammer B (2001)
On the Generalization Ability of Recurrent Networks.
In: Artificial Neural Networks. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 731-736.
PUB
 
[24]
2001 | Conference Paper | PUB-ID: 1993768
Hammer B, Villmann T (2001)
Input Pruning for Neural Gas Architectures.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001). Brussels, Belgium: D facto publications: 283-288.
PUB
 
[23]
2001 | Conference Paper | PUB-ID: 1994042
Strickert M, Bojer T, Hammer B (2001)
Generalized Relevance LVQ for Time Series.
In: Artificial Neural Networks. International Conference. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 677-683.
PUB
 
[22]
2001 | Journal Article | PUB-ID: 1993510
Hammer B (2001)
Generalization Ability of Folding Networks.
IEEE Trans. Knowl. Data Eng. 13(2): 196-206.
PUB
 
[21]
2001 | Conference Paper | PUB-ID: 1993739
Hammer B, Villmann T (2001)
Estimating Relevant Input Dimensions for Self-Organizing Algorithms.
In: Advances in Self-Organising Maps. Allinson NM, Yin H, Allinson L, Slack J (Eds); London: Springer: 173-180.
PUB
 
[20]
2001 | Journal Article | PUB-ID: 1994123
Vidyasagar M, Balaji S, Hammer B (2001)
Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures.
System and Control Letters 42: 151-157.
PUB
 
[19]
2000 | Journal Article | PUB-ID: 1993512
Hammer B (2000)
On the approximation capability of recurrent neural networks.
Neurocomputing 31(1-4): 107-123.
PUB
 
[18]
2000 | Conference Paper | PUB-ID: 1993400
DasGupta B, Hammer B (2000)
On Approximate Learning by Multi-layered Feedforward Circuits.
In: Algorithmic Learning Theory, 11th International Conference. Proceedings. Lecture Notes in Computer Science, 1968. Arimura H, Jain S, Sharma A (Eds); Berlin: Springer: 264-278.
PUB
 
[17]
2000 | Conference Paper | PUB-ID: 1993495
Hammer B (2000)
Neural networks classifying symbolic data.
In: ICML workshop on attribute-value and relational learning: crossing the boundaries. de Raedt L, Kramer S (Eds); 61-65.
PUB
 
[16]
2000 | Conference Paper | PUB-ID: 1993499
Hammer B (2000)
Limitations of hybrid systems.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 213-218.
PUB
 
[15]
2000 | Book | PUB-ID: 1993514
Hammer B (2000)
Learning with Recurrent Neural Networks. Lecture Notes in Control and Information Sciences, 254.
Berlin: Springer.
PUB
 
[14]
2000 | Conference Paper | PUB-ID: 1993479
Hammer B (2000)
Approximation and generalization issues of recurrent networks dealing with structured data.
In: ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms. Frasconi P, Sperduti A, Gori M (Eds);.
PUB
 
[13]
1999 | Journal Article | PUB-ID: 1993516
Hammer B (1999)
On the learnability of recursive data.
Mathematics of Control, Signals and Systems 12: 62-79.
PUB
 
[12]
1999 | Report | PUB-ID: 1993409
DasGupta B, Hammer B (1999)
Hardness of approximation of the loading problem for multi-layered feedforward neural networks.
DIMACS Center, Rutgers University.
PUB
 
[11]
1999 | Conference Paper | PUB-ID: 1993502
Hammer B (1999)
Approximation capabilities of folding networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 33-38.
PUB
 
[10]
1998 | Conference Paper | PUB-ID: 1993505
Hammer B (1998)
Training a sigmoidal network is difficult.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 255-260.
PUB
 
[9]
1998 | Conference Paper | PUB-ID: 1993518
Hammer B (1998)
Some complexity results for perceptron networks.
In: International Conference on artificial Neural Networks. 639-644.
PUB
 
[8]
1998 | Conference Paper | PUB-ID: 1993484
Hammer B (1998)
On the Approximation Capability of Recurrent Neural Networks.
In: Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998). Heiss M (Ed); ICSC Academic Press: 512-518.
PUB
 
[7]
1997 | Report | PUB-ID: 1993524
Hammer B (1997)
On the generalization ability of simple recurrent neural networks. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[6]
1997 | Conference Paper | PUB-ID: 1993684
Hammer B, Sperschneider V (1997)
Neural networks can approximate mappings on structured objects.
In: International conference on Computational Intelligence and Neural Networks. Wang PP (Ed); 211-214.
PUB
 
[5]
1997 | Report | PUB-ID: 1993520
Hammer B (1997)
Learning recursive data is intractable. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[4]
1997 | Conference Paper | PUB-ID: 1993526
Hammer B (1997)
Generalization of Elman Networks.
In: Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327. Berlin: Springer: 409-414.
PUB
 
[3]
1997 | Report | PUB-ID: 1993522
Hammer B (1997)
A NP-hardness result for a sigmoidal 3-node neural network. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[2]
1996 | Report | PUB-ID: 1993528
Hammer B (1996)
Universal approximation of mappings on structured objects using the folding architecture. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[1]
1996 | Book | PUB-ID: 1994039
Sperschneider V, Hammer B (1996)
Theoretische Informatik. Eine problemorientierte Einführung.
erlin: Springer.
PUB
 

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[332]
2017 | Conference Paper | PUB-ID: 2913752
Göpfert JP, Göpfert C, Botsch M, Hammer B (Accepted)
Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction.
In: Proceedings of IEEE Symposium Series on Computational Intelligence. .
PUB
 
[331]
2017 | Conference Paper | PUB-ID: 2908201
Göpfert C, Pfannschmidt L, Hammer B (2017)
Feature Relevance Bounds for Linear Classification.
In: Proceedings of the ESANN. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
PUB | Files available
 
[330]
2017 | Journal Article | PUB-ID: 2913389
Paaßen B, Hammer B, Price T, Barnes T, Gross S, Pinkwart N (Submitted)
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces.
Journal of Educational Datamining.
PUB | arXiv
 
[329]
2017 | Journal Article | PUB-ID: 2911900
Paaßen B, Göpfert C, Hammer B (2017)
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces.
Neural Processing Letters: 1-21.
PUB | DOI | arXiv
 
[328]
2017 | Conference Paper | PUB-ID: 2909037
Prahm C, Schulz A, Paaßen B, Aszmann O, Hammer B, Dorffner G (2017)
Echo State Networks as Novel Approach for Low-Cost Myoelectric Control.
In: Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). ten Telje A, Popow C, Holmes JH, Sacchi L (Eds); Lecture Notes in Computer Science, 10259. Springer: 338--342.
PUB | Files available | DOI
 
[327]
2017 | Journal Article | PUB-ID: 2909372
Schulz A, Brinkrolf J, Hammer B (2017)
Efficient Kernelization of Discriminative Dimensionality Reduction.
Neurocomputing accepted.
PUB | PDF | DOI
 
[326]
2017 | Conference Paper | PUB-ID: 2909369
Paaßen B, Schulz A, Hahne J, Hammer B (2017)
An EM transfer learning algorithm with applications in bionic hand prostheses.
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.
PUB | PDF
 
[325]
2017 | Conference Paper | PUB-ID: 2909371
Biehl M, Hammer B, Villmann T (2017)
Prototype based models for the supervised learning of classificaton schemes.
In: Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016. in press.
PUB
 
[324]
2017 | Conference Paper | PUB-ID: 2909370
Frenay B, Hammer B (2017)
Label-Noise-Tolerant Classification for Streaming Data.
In: IEEE International Joint Conference on Neural Neworks. .
PUB
 
[323]
2016 | Conference Paper | PUB-ID: 2900676
Paaßen B, Göpfert C, Hammer B (2016)
Gaussian process prediction for time series of structured data.
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges: 41-46.
PUB | PDF
 
[322]
2016 | Conference Paper | PUB-ID: 2905855
Paaßen B, Schulz A, Hammer B (2016)
Linear Supervised Transfer Learning for Generalized Matrix LVQ.
In: Proceedings of the Workshop New Challenges in Neural Computation 2016. Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports, 11-18.
PUB
 
[321]
2016 | Conference Paper | PUB-ID: 2907624
Losing V, Hammer B, Wersing H (2016)
Choosing the Best Algorithm for an Incremental On-line Learning Task.
Presented at the European Symposium on Artificial Neural Networks, Brügge.
PUB | PDF
 
[320]
2016 | Conference Paper | PUB-ID: 2907622
Losing V, Hammer B, Wersing H (2016)
KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift.
In: 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE: 291-300.
PUB | PDF | DOI
 
[319]
2016 | Conference Paper | PUB-ID: 2909367
Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B (2016)
Local Reject Option for Deterministic Multi-class SVM.
In: Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. 251--258.
PUB | DOI
 
[318]
2016 | Conference Paper | PUB-ID: 2909366
Villmann T, Kaden M, Bohnsack A, Villmann JM, Drogies T, Saralajew S, Hammer B (2016)
Self-Adjusting Reject Options in Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016. Merényi E, Mendenhall MJ, O'Driscoll P (Eds); Advances in Intelligent Systems and Computing, 428. Springer International Publishing: 269-279.
PUB | DOI
 
[317]
2016 | Journal Article | PUB-ID: 2910957
Biehl M, Hammer B, Villmann T (2016)
Prototype-based models in machine learning.
Wiley Interdisciplinary Reviews: Cognitive Science 7(2): 92-111.
PUB | DOI
 
[316]
2016 | Conference Paper | PUB-ID: 2909368
Geppert er, Hammer B (2016)
Incremental learning algorithms and applications.
In: ESANN. .
PUB
 
[315]
2016 | Conference Paper | PUB-ID: 2909365
Brinkrolf J, Mittag T, Joppen R, Dr\ A, Pietsch K-H, Hammer B (2016)
Virtual optimisation for improved production planning.
In: New Challenges in Neural Computation. .
PUB
 
[314]
2016 | Journal Article | PUB-ID: 2907633
Lux M, Krüger J, Rinke C, Maus I, Schlüter A, Woyke T, Sczyrba A, Hammer B (2016)
acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data.
BMC Bioinformatics 17: 543.
PUB | PDF | DOI | WoS | PubMed | Europe PMC
 
[313]
2016 | Conference Paper | PUB-ID: 2908455
Losing V, Hammer B, Wersing H (2016)
Dedicated Memory Models for Continual Learning in the Presence of Concept Drift.
Presented at the Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona.
PUB | PDF
 
[312]
2016 | Conference Paper | PUB-ID: 2904909
Schulz A, Hammer B (2016)
Discriminative Dimensionality Reduction in Kernel Space.
In: ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016. i6doc.com.
PUB | PDF
 
[311]
2016 | Conference Paper | PUB-ID: 2904509
Paaßen B, Jensen J, Hammer B (2016)
Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming.
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
PUB
 
[310]
2016 | Conference Paper | PUB-ID: 2905729
Göpfert C, Paaßen B, Hammer B (2016)
Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning.
In: Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part 1. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9886. Springer Nature: 510-517.
PUB | PDF | DOI
 
[309]
2016 | Journal Article | PUB-ID: 2905193
Fischer L, Hammer B, Wersing H (2016)
Optimal local rejection for classifiers.
Neurocomputing 214: 445-457.
PUB | DOI | WoS
 
[308]
2016 | Conference Paper | PUB-ID: 2904178
Prahm C, Paaßen B, Schulz A, Hammer B, Aszmann O (2016)
Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift.
In: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Ibáñez J, Gonzáles-Vargas J, Azorín JM, Akay M, Pons JL (Eds); Springer: 153--157.
PUB | PDF | DOI
 
[307]
2016 | Conference Paper | PUB-ID: 2904469
Hosseini B, Hülsmann F, Botsch M, Hammer B (2016)
Non-Negative Kernel Sparse Coding for the Analysis of Motion Data.
Lecture Notes in Computer Science 9887: 506-514.
PUB | PDF | DOI
 
[306]
2016 | Conference Paper | PUB-ID: 2905195
Fischer L, Hammer B, Wersing H (2016)
Online Metric Learning for an Adaptation to Confidence Drift.
In: Proceedings of International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE: 748-755.
PUB
 
[305]
2016 | Journal Article | PUB-ID: 2783224
Paaßen B, Mokbel B, Hammer B (2016)
Adaptive structure metrics for automated feedback provision in intelligent tutoring systems.
Neurocomputing 192(SI): 3-13.
PUB | PDF | DOI | WoS
 
[304]
2016 | Journal Article | PUB-ID: 2903457
Schleif F-M, Hammer B, Gonzalez Monroy J, Gonzalez Jimenez J, Blanco-Claraco J-L, Biehl M, Petkov N (2016)
Odor recognition in robotics applications by discriminative time-series modeling.
PATTERN ANALYSIS AND APPLICATIONS 19(1): 207-220.
PUB | DOI | WoS
 
[303]
2015 | Conference Paper | PUB-ID: 2776021
Losing V, Hammer B, Wersing H (2015)
Interactive Online Learning for Obstacle Classification on a Mobile Robot.
Presented at the International Joint Conference on Neural Networks, Killarney, Ireland.
PUB | PDF | DOI
 
[302]
2015 | Conference Paper | PUB-ID: 2724156
Paaßen B, Mokbel B, Hammer B (2015)
Adaptive structure metrics for automated feedback provision in Java programming.
In: Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 307-312.
PUB | PDF
 
[301]
2015 | Journal Article | PUB-ID: 2710031
Mokbel B, Paaßen B, Schleif F-M, Hammer B (2015)
Metric learning for sequences in relational LVQ.
Neurocomputing 169: 306-322.
PUB | PDF | DOI | WoS
 
[300]
2015 | Conference Paper | PUB-ID: 2910954
Biehl M, Hammer B, Schleif F-M, Schneider P, Villmann T (2015)
Stationarity of Matrix Relevance LVQ.
In: 2015 International Joint Conference on Neural Networks (IJCNN). IEEE.
PUB | DOI
 
[299]
2015 | Journal Article | PUB-ID: 2909364
Hammer B, Toussaint M (2015)
Special Issue on Autonomous Learning.
{KI} 29(4): 323--327.
PUB | DOI
 
[298]
2015 | Conference Paper | PUB-ID: 2901612
Lux M, Sczyrba A, Hammer B (2015)
Automatic discovery of metagenomic structure.
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE).
PUB | DOI
 
[297]
2015 | Journal Article | PUB-ID: 2909226
Gisbrecht A, Hammer B (2015)
Data visualization by nonlinear dimensionality reduction.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5(2): 51-73.
PUB | DOI
 
[296]
2015 | Report | PUB-ID: 2901613
Lux M, Hammer B, Sczyrba A (2015)
Automated Contamination Detection in Single-Cell Sequencing.
Cold Spring Harbor Laboratory Press.
PUB | DOI
 
[295]
2015 | Conference Paper | PUB-ID: 2783165
Hosseini B, Hammer B (2015)
Efficient Metric Learning for the Analysis of Motion Data.
Presented at the Data Science and Advanced Analytics (DSAA), Paris, France.
PUB | PDF | DOI
 
[294]
2015 | Journal Article | PUB-ID: 2671047
Gisbrecht A, Schulz A, Hammer B (2015)
Parametric nonlinear dimensionality reduction using kernel t-SNE.
Neurocomputing 147: 71-82.
PUB | PDF | DOI | WoS
 
[293]
2015 | Journal Article | PUB-ID: 2772407
Nebel D, Hammer B, Frohberg K, Villmann T (2015)
Median variants of learning vector quantization for learning of dissimilarity data.
Neurocomputing 169: 295-305.
PUB | DOI | WoS
 
[292]
2015 | Journal Article | PUB-ID: 2759763
Schleif F-M, Zhu X, Hammer B (2015)
Sparse conformal prediction for dissimilarity data.
Annals of Mathematics and Artificial Intelligence 74(1-2): 95-116.
PUB | DOI | WoS
 
[291]
2015 | Conference Paper | PUB-ID: 2903777
Schulz A, Mokbel B, Biehl M, Hammer B (2015)
Inferring Feature Relevances From Metric Learning.
In: 2015 IEEE Symposium Series on Computational Intelligence. Institute of Electrical & Electronics Engineers (IEEE).
PUB | DOI
 
[290]
2015 | Preprint | PUB-ID: 2774656
Fischer L, Hammer B, Wersing H (2015)
Optimum Reject Options for Prototype-based Classification.
PUB | arXiv
 
[289]
2015 | Conference Paper | PUB-ID: 2774707
Fischer L, Hammer B, Wersing H (2015)
Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation.
In: ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 7-12.
PUB
 
[288]
2015 | Conference Paper | PUB-ID: 2774721
Fischer L, Hammer B, Wersing H (2015)
Combining Offline and Online Classifiers for Life-long Learning.
In: IJCNN, International Joint Conference on Neural Networks. 2808-2815.
PUB
 
[287]
2015 | Journal Article | PUB-ID: 2772413
Fischer L, Hammer B, Wersing H (2015)
Efficient rejection strategies for prototype-based classification.
Neurocomputing 169: 334-342.
PUB | DOI | WoS
 
[286]
2015 | Book Chapter | PUB-ID: 2900303
Schulz A, Hammer B (2015)
Visualization of Regression Models Using Discriminative Dimensionality Reduction.
In: Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, 9257. Springer Science + Business Media: 437-449.
PUB | PDF | DOI
 
[285]
2015 | Journal Article | PUB-ID: 2766822
Schulz A, Gisbrecht A, Hammer B (2015)
Using Discriminative Dimensionality Reduction to Visualize Classifiers.
Neural Processing Letters 42(1): 27-54.
PUB | PDF | DOI | WoS
 
[284]
2015 | Conference Paper | PUB-ID: 2900318
Schulz A, Hammer B (2015)
Metric Learning in Dimensionality Reduction.
In: Proceedings of the International Conference on Pattern Recognition Applications and Methods. Scitepress: 232-239.
PUB | DOI
 
[283]
2015 | Conference Paper | PUB-ID: 2900319
Schulz A, Hammer B (2015)
Discriminative dimensionality reduction for regression problems using the Fisher metric.
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE): 1-8.
PUB | DOI
 
[282]
2015 | Conference Paper | PUB-ID: 2900325
Blöbaum P, Schulz A, Hammer B (2015)
Unsupervised Dimensionality Reduction for Transfer Learning.
In: Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco: 507-512.
PUB | PDF
 
[281]
2015 | Journal Article | PUB-ID: 2752955
Walter O, Häb-Umbach R, Mokbel B, Paaßen B, Hammer B (2015)
Autonomous Learning of Representations.
KI - Künstliche Intelligenz: 1-13.
PUB | PDF | DOI
 
[280]
2015 | Conference Paper | PUB-ID: 2762087
Paaßen B, Mokbel B, Hammer B (2015)
A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems.
In: Proceedings of the 8th International Conference on Educational Data Mining. Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M (Eds); International Educational Datamining Society: 632-632.
PUB
 
[279]
2015 | Journal Article | PUB-ID: 2752948
Gross S, Mokbel B, Hammer B, Pinkwart N (2015)
Learning Feedback in Intelligent Tutoring Systems.
KI - Künstliche Intelligenz: 1-6.
PUB | PDF | DOI
 
[278]
2015 | Journal Article | PUB-ID: 2695196
Hofmann D, Gisbrecht A, Hammer B (2015)
Efficient approximations of robust soft learning vector quantization for non-vectorial data.
Neurocomputing 147: 96-106.
PUB | DOI | WoS
 
[277]
2014 | Journal Article | PUB-ID: 2694967
Jin Y, Hammer B (2014)
Computational Intelligence in Big Data.
IEEE Computational Intelligence Magazine 9(3): 12-13.
PUB | DOI | WoS
 
[276]
2014 | Conference Paper | PUB-ID: 2909361
Hammer B, Nebel D, Riedel M, Villmann T (2014)
Generative versus Discriminative Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, {WSOM} 2014, Mittweida, Germany, July, 2-4, 2014. 123--132.
PUB | DOI
 
[275]
2014 | Conference Paper | PUB-ID: 2909360
Gross S, Mokbel B, Hammer B, Pinkwart N (2014)
How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning.
In: Intelligent Tutoring Systems. Trausan-Matu S, Elizabeth Boyer K, E. Crosby M, Panourgia K (Eds); Lecture Notes in Computer Science, 8474. Springer: 340-347.
PUB
 
[274]
2014 | Conference Paper | PUB-ID: 2673548
Fischer L, Hammer B, Wersing H (2014)
Rejection strategies for learning vector quantization.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 41-46.
PUB
 
[273]
2014 | Conference Paper | PUB-ID: 2774498
Fischer L, Hammer B, Wersing H (2014)
Local Rejection Strategies for Learning Vector Quantization.
In: Artificial Neural Networks and Machine Learning – ICANN 2014. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa AEP (Eds); Lecture Notes in Computer Science, 8681. 563-570.
PUB | DOI
 
[272]
2014 | Conference Paper | PUB-ID: 2774643
Fischer L, Nebel D, Villmann T, Hammer B, Wersing H (2014)
Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches.
In: Advances in Self-Organizing Maps and Learning Vector Quantization. Villmann T, Schleif F-M, Kaden M, Lange M (Eds); Advances in Intelligent Systems and Computing, 295. 109-118.
PUB | DOI
 
[271]
2014 | Conference Paper | PUB-ID: 2900320
Frenay B, Hofmann D, Schulz A, Biehl M, Hammer B (2014)
Valid interpretation of feature relevance for linear data mappings.
In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Institute of Electrical & Electronics Engineers (IEEE): 149-156.
PUB | DOI
 
[270]
2014 | Book Chapter | PUB-ID: 2900324
Gisbrecht A, Schulz A, Hammer B (2014)
Discriminative Dimensionality Reduction for the Visualization of Classifiers.
In: Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing, 318. Springer Science + Business Media: 39-56.
PUB | DOI
 
[269]
2014 | Journal Article | PUB-ID: 2678214
Hofmann D, Schleif F-M, Paaßen B, Hammer B (2014)
Learning interpretable kernelized prototype-based models.
Neurocomputing 141: 84-96.
PUB | DOI | WoS
 
[268]
2014 | Journal Article | PUB-ID: 2734058
Gross S, Mokbel B, Paaßen B, Hammer B, Pinkwart N (2014)
Example-based feedback provision using structured solution spaces.
International Journal of Learning Technology 9(3): 248-280.
PUB | DOI
 
[267]
2014 | Conference Paper | PUB-ID: 2710067
Mokbel B, Paaßen B, Hammer B (2014)
Efficient Adaptation of Structure Metrics in Prototype-Based Classification.
In: Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa A (Eds); Lecture Notes in Computer Science, 8681. Springer: 571-578.
PUB | PDF | DOI
 
[266]
2014 | Conference Paper | PUB-ID: 2673554
Mokbel B, Paaßen B, Hammer B (2014)
Adaptive distance measures for sequential data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 265-270.
PUB | PDF
 
[265]
2014 | Journal Article | PUB-ID: 2672504
Zhu X, Schleif F-M, Hammer B (2014)
Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data.
Pattern Recognition Lettters 49: 138-145.
PUB | DOI | WoS
 
[264]
2014 | Journal Article | PUB-ID: 2615730
Hammer B, Hofmann D, Schleif F-M, Zhu X (2014)
Learning vector quantization for (dis-)similarities.
NeuroComputing 131: 43-51.
PUB | DOI | WoS
 
[263]
2014 | Conference Paper | PUB-ID: 2673559
Hammer B, He H, Martinetz T (2014)
Learning and modeling big data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 343-352.
PUB
 
[262]
2014 | Conference Paper | PUB-ID: 2673557
Schulz A, Gisbrecht A, Hammer B (2014)
Relevance learning for dimensionality reduction.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 165-170.
PUB
 
[261]
2014 | Conference Paper | PUB-ID: 2673545
Nebel D, Hammer B, Villmann T (2014)
Supervised Generative Models for Learning Dissimilarity Data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 35-40.
PUB
 
[260]
2013 | Conference Paper | PUB-ID: 2670686
Gross S, Mokbel B, Hammer B, Pinkwart N (2013)
Towards a Domain-Independent ITS Middleware Architecture.
In: 2013 IEEE 13th International Conference on Advanced Learning Technologies. 408-409.
PUB | DOI | WoS
 
[259]
2013 | Conference Paper | PUB-ID: 2625185
Mokbel B, Gross S, Paaßen B, Pinkwart N, Hammer B (2013)
Domain-Independent Proximity Measures in Intelligent Tutoring Systems.
In: Proceedings of the 6th International Conference on Educational Data Mining (EDM). D'Mello SK, Calvo RA, Olney A (Eds); 334-335.
PUB
 
[258]
2013 | Conference Paper | PUB-ID: 2909359
Nebel D, Hammer B, Villmann T (2013)
A Median Variant of Generalized Learning Vector Quantization.
In: ICONIP (2). 19-26.
PUB
 
[257]
2013 | Conference Paper | PUB-ID: 2909358
Strickert M, Hammer B, Villmann T, Biehl M (2013)
Regularization and Improved Interpretation of Linear Data Mappings and Adaptive Distance Measures.
In: IEEE SSCI CIDM 2013. IEEE Computational Intelligence Society: 10-17.
PUB
 
[256]
2013 | Conference Paper | PUB-ID: 2909357
Gisbrecht A, Hammer B, Mokbel B, Sczyrba A (2013)
Nonlinear dimensionality reduction for cluster identification in metagenomic samples.
In: IV. 174-179.
PUB
 
[255]
2013 | Conference Paper | PUB-ID: 2622456
Schulz A, Gisbrecht A, Hammer B (2013)
Using Nonlinear Dimensionality Reduction to Visualize Classifiers.
In: Advances in computational intelligence. Proceedings. Vol 1. Rojas I, Joya G, Gabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 59-68.
PUB | DOI | WoS
 
[254]
2013 | Journal Article | PUB-ID: 2612736
Mokbel B, Lueks W, Gisbrecht A, Hammer B (2013)
Visualizing the quality of dimensionality reduction.
Neurocomputing 112: 109-123.
PUB | DOI | WoS
 
[253]
2013 | Journal Article | PUB-ID: 2607146
Hammer B, Keim D, Lawrence N, Lebanon G (2013)
Preface: Intelligent interactive data visualization.
Data Mining and Knowledge Discovery 27(1): 1-3.
PUB | DOI | WoS
 
[252]
2013 | Conference Paper | PUB-ID: 2622454
Hammer B, Gisbrecht A, Schulz A (2013)
Applications of discriminative dimensionality reduction.
In: Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods. SCITEPRESS: 33-41.
PUB | DOI
 
[251]
2013 | Conference Paper | PUB-ID: 2622467
Schulz A, Gisbrecht A, Hammer B (2013)
Classifier inspection based on different discriminative dimensionality reductions.
In: Workshop NC^2 2013. TR Machine Learning Reports: 77-86.
PUB
 
[250]
2013 | Conference Paper | PUB-ID: 2625202
Schleif F-M, Zhu X, Hammer B (2013)
Sparse prototype representation by core sets.
In: IDEAL 2013. Hujun Yin et.al (Ed);.
PUB
 
[249]
2013 | Conference Paper | PUB-ID: 2625194
Gisbrecht A, Miche Y, Hammer B, Lendasse A (2013)
Visualizing Dependencies of Spectral Features using Mutual Information.
In: ESANN, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 573-578.
PUB
 
[248]
2013 | Conference Paper | PUB-ID: 2625199
Hofmann D, Hammer B (2013)
Sparse approximations for kernel learning vector quantization.
In: ESANN. .
PUB
 
[247]
2013 | Conference Paper | PUB-ID: 2625207
Gross S, Mokbel B, Hammer B, Pinkwart N (2013)
Towards Providing Feedback to Students in Absence of Formalized Domain Models.
In: AIED. 644-648.
PUB
 
[246]
2013 | Conference Paper | PUB-ID: 2615717
Zhu X, Schleif F-M, Hammer B (2013)
Secure Semi-supervised Vector Quantization for Dissimilarity Data.
In: IWANN (1). Rojas I, Joya G, Cabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 347-356.
PUB | DOI
 
[245]
2013 | Conference Paper | PUB-ID: 2615701
Zhu X, Schleif F-M, Hammer B (2013)
Semi-Supervised Vector Quantization for proximity data.
In: Proceedings of ESANN 2013. 89-94.
PUB
 
[244]
2012 | Journal Article | PUB-ID: 2625232
Gisbrecht A, Mokbel B, Schleif F-M, Zhu X, Hammer B (2012)
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(5).
PUB | DOI | WoS | PubMed | Europe PMC
 
[243]
2012 | Journal Article | PUB-ID: 2509858
Kaestner M, Hammer B, Biehl M, Villmann T (2012)
Functional relevance learning in generalized learning vector quantization.
Neurocomputing 90: 85-95.
PUB | DOI | WoS
 
[242]
2012 | Conference Paper | PUB-ID: 2909356
Mokbel B, Lueks W, Gisbrecht A, Biehl M, Hammer B (2012)
Visualizing the quality of dimensionality reduction.
In: ESANN 2012. Verleysen M (Ed); 179--184.
PUB
 
[241]
2012 | Journal Article | PUB-ID: 2671281
Hammer B, Villmann T (2012)
Special issue on new challenges in neural computation 2012.
Neurocomputing 131: 1-1.
PUB | DOI | WoS
 
[240]
2012 | Conference Paper | PUB-ID: 2622449
Schulz A, Gisbrecht A, Bunte K, Hammer B (2012)
How to visualize a classifier?
In: Proceedings of the Workshop - New Challenges in Neural Computation 2012. Machine Learning Reports: 73-83.
PUB
 
[239]
2012 | Conference Paper | PUB-ID: 2622453
Hammer B, Gisbrecht A, Schulz A (2012)
How to Visualize Large Data Sets?
Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile.
PUB | DOI
 
[238]
2012 | Conference Paper | PUB-ID: 2536426
Mokbel B, Gross S, Lux M, Pinkwart N, Hammer B (2012)
How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
In: Artificial Neural Networks in Pattern Recognition. 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Artificial Intelligence, 7477. Springer Berlin Heidelberg: 1-13.
PUB | PDF | DOI
 
[237]
2012 | Conference Paper | PUB-ID: 2671172
Hofmann D, Gisbrecht A, Hammer B (2012)
Discriminative probabilistic prototype based models in kernel space.
In: Workshop NC^2 2012. TR Machine Learning Reports.
PUB
 
[236]
2012 | Journal Article | PUB-ID: 2625223
Hammer B (2012)
Challenges in Neural Computation.
Künstliche Intelligenz : KI 26(4): 333-340.
PUB | DOI
 
[235]
2012 | Journal Article | PUB-ID: 2625225
Hammer B (2012)
Special Issue on Neural Learning Paradigms.
Künstliche Intelligenz :KI 26(4): 329-332.
PUB | DOI
 
[234]
2012 | Conference Paper | PUB-ID: 2625238
Hofmann D, Gisbrecht A, Hammer B (2012)
Efficient Approximations of Kernel Robust Soft LVQ.
In: WSOM. .
PUB
 
[233]
2012 | Conference Paper | PUB-ID: 2625265
Gisbrecht A, Sovilj D, Hammer B, Lendasse A (2012)
Relevance learning for time series inspection.
In: ESANN 2012. Verleysen M (Ed); 489-494.
PUB
 
[232]
2012 | Conference Paper | PUB-ID: 2625254
Hofmann D, Hammer B (2012)
Kernel Robust Soft Learning Vector Quantization.
In: Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Computer Science, 7477. Springer: 14-23.
PUB
 
[231]
2012 | Conference Paper | PUB-ID: 2625271
Bouveyron C, Hammer B, Villmann T (2012)
Recent developments in clustering algorithms.
In: ESANN 2012. Verleysen M (Ed); 447-458.
PUB
 
[230]
2012 | Conference Paper | PUB-ID: 2625247
Gisbrecht A, Hofmann D, Hammer B (2012)
Discriminative Dimensionality Reduction Mappings.
In: Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Hollmén J, Klawonn F, Tucker A (Eds); Lecture Notes in Computer Science, 7619. Springer: 126-138.
PUB
 
[229]
2012 | Conference Paper | PUB-ID: 2625276
Gisbrecht A, Mokbel B, Hammer B (2012)
Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction.
In: IJCNN. .
PUB
 
[228]
2012 | Conference Paper | PUB-ID: 2625242
Gross S, Mokbel B, Hammer B, Pinkwart N (2012)
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI. 27-38.
PUB
 
[227]
2012 | Conference Paper | PUB-ID: 2625260
Gisbrecht A, Lueks W, Mokbel B, Hammer B (2012)
Out-of-sample kernel extensions for nonparametric dimensionality reduction.
In: ESANN 2012. 531-536.
PUB
 
[226]
2012 | Conference Paper | PUB-ID: 2615750
Schleif F-M, Zhu X, Gisbrecht A, Hammer B (2012)
Fast approximated relational and kernel clustering.
In: Proceedings of ICPR 2012. IEEE: 1229-1232.
PUB
 
[225]
2012 | Conference Paper | PUB-ID: 2615756
Schleif F-M, Zhu X, Hammer B (2012)
Soft Competitive Learning for large data sets.
In: Proceedings of MCSD 2012. 141-151.
PUB | DOI
 
[224]
2012 | Conference Paper | PUB-ID: 2534877
Schleif F-M, Mokbel B, Gisbrecht A, Theunissen L, Dürr V, Hammer B (2012)
Learning Relevant Time Points for Time-Series Data in the Life Sciences.
In: ICANN (2). Lecture Notes in Computer Science, 7553. 531-539.
PUB | DOI
 
[223]
2012 | Conference Paper | PUB-ID: 2536437
Gross S, Zhu X, Hammer B, Pinkwart N (2012)
Cluster based feedback provision strategies in intelligent tutoring systems.
In: Proceedings of the 11th international conference on Intelligent Tutoring Systems. Berlin, Heidelberg: Springer-Verlag: 699-700.
PUB | PDF | DOI
 
[222]
2012 | Conference Paper | PUB-ID: 2536444
Gross S, Mokbel B, Hammer B, Pinkwart N (2012)
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI 2012: Die 10. e-Learning Fachtagung Informatik. Desel J, Haake JM, Spannagel C (Eds); Hagen, Germany: Köllen: 27-38.
PUB | PDF
 
[221]
2012 | Conference Paper | PUB-ID: 2534905
Schleif F-M, Gisbrecht A, Hammer B (2012)
Relevance learning for short high-dimensional time series in the life sciences.
In: IJCNN. 1-8.
PUB | DOI
 
[220]
2012 | Journal Article | PUB-ID: 2489405
Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2012)
Limited Rank Matrix Learning, discriminative dimension reduction and visualization.
Neural Networks 26: 159-173.
PUB | DOI | WoS | PubMed | Europe PMC
 
[219]
2012 | Journal Article | PUB-ID: 2509852
Zhu X, Gisbrecht A, Schleif F-M, Hammer B (2012)
Approximation techniques for clustering dissimilarity data.
Neurocomputing 90: 72-84.
PUB | DOI | WoS
 
[218]
2012 | Conference Paper | PUB-ID: 2534910
Zhu X, Schleif F-M, Hammer B (2012)
Patch Processing for Relational Learning Vector Quantization.
In: ISNN (1). 55-63.
PUB | DOI
 
[217]
2012 | Conference Paper | PUB-ID: 2534888
Schleif F-M, Zhu X, Hammer B (2012)
A Conformal Classifier for Dissimilarity Data.
In: AIAI (2). 234-243.
PUB | DOI
 
[216]
2012 | Conference Paper | PUB-ID: 2534868
Hammer B, Mokbel B, Schleif F-M, Zhu X (2012)
White Box Classification of Dissimilarity Data.
In: HAIS (1). 309-321.
PUB | DOI | WoS
 
[215]
2012 | Journal Article | PUB-ID: 2534839
Gisbrecht A, Mokbel B, Schleif F-M, Zhu X, Hammer B (2012)
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(05): 1250021.
PUB | DOI | WoS | PubMed | Europe PMC
 
[214]
2012 | Journal Article | PUB-ID: 2474292
Bunte K, Biehl M, Hammer B (2012)
A General Framework for Dimensionality-Reducing Data Visualization Mapping.
Neural Computation 24(3): 771-804.
PUB | DOI | WoS
 
[213]
2011 | Journal Article | PUB-ID: 2309980
Schleif F-M, Villmann T, Hammer B, Schneider P (2011)
Efficient Kernelized Prototype-based Classification.
International Journal of Neural Systems 21(06): 443-457.
PUB | DOI | WoS | PubMed | Europe PMC
 
[212]
2011 | Conference Paper | PUB-ID: 2276480
Gisbrecht A, Schleif F-M, Zhu X, Hammer B (2011)
Linear time heuristics for topographic mapping of dissimilarity data.
In: Intelligent Data Engineering and Automated Learning - IDEAL 2011: IDEAL 2011, 12th international conference, Norwich, UK, September 7 - 9, 2011 ; proceedings. Lecture Notes in Computer Science, 6936. Berlin, Heidelberg: Springer: 25-33.
PUB | DOI
 
[211]
2011 | Journal Article | PUB-ID: 2534994
Schleif F-M, Gisbrecht A, Hammer B (2011)
Supervised learning of short and high-dimensional temporal sequences for life science measurements.
CoRR 2011(1; abs/1110.2416.).
PUB
 
[210]
2011 | Journal Article | PUB-ID: 2276540
Gisbrecht A, Hammer B (2011)
Relevance learning in generative topographic mapping.
Neurocomputing 74(9): 1351-1358.
PUB | DOI | WoS
 
[209]
2011 | Journal Article | PUB-ID: 2276506
Bunte K, Hammer B, Villmann T, Biehl M, Wismueller A (2011)
Neighbor embedding XOM for dimension reduction and visualization.
Neurocomputing 74(9): 1340-1350.
PUB | DOI | WoS
 
[208]
2011 | Journal Article | PUB-ID: 2276531
Gisbrecht A, Mokbel B, Hammer B (2011)
Relational Generative Topographic Mapping.
Neurocomputing 74(9): 1359-1371.
PUB | DOI | WoS
 
[207]
2011 | Conference Paper | PUB-ID: 2091665
Zhu X, Hammer B (2011)
Patch Affinity Propagation.
Presented at the 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium.
PUB
 
[206]
2011 | Conference Paper | PUB-ID: 2276500
Kaestner M, Hammer B, Biehl M, Villmann T (2011)
Generalized Functional Relevance Learning Vector Quantization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 93-98.
PUB
 
[205]
2011 | Conference Paper | PUB-ID: 2276492
Schleif F-M, Gisbrecht A, Hammer B (2011)
Accelerating Kernel Neural Gas.
In: ICANN'2011. Kaski S, Honkela T, Gitolami M, Dutch W (Eds);.
PUB
 
[204]
2011 | Conference Paper | PUB-ID: 2276485
Hammer B, Gisbrecht A, Hasenfuss A, Mokbel B, Schleif F-M, Zhu X (2011)
Topographic Mapping of Dissimilarity Data.
In: WSOM'11. .
PUB
 
[203]
2011 | Conference Paper | PUB-ID: 2276527
Bunte K, Biehl M, Hammer B (2011)
Dimensionality Reduction Mappings.
In: IEEE Symposium on Computational Intelligence and Data Mining. pp. 349-356.
PUB | DOI
 
[202]
2011 | Conference Paper | PUB-ID: 2276522
Gisbrecht A, Hammer B, Schleif F-M, Zhu X (2011)
Accelerating dissimilarity clustering for biomedical data analysis.
In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp.154-161.
PUB
 
[201]
2011 | Conference Paper | PUB-ID: 2276517
Bunte K, Biehl M, Hammer B (2011)
Supervised dimension reduction mappings.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 281-286.
PUB
 
[200]
2011 | Conference Paper | PUB-ID: 2276512
Hammer B, Biehl M, Bunte K, Mokbel B (2011)
A general framework for dimensionality reduction for large data sets.
In: WSOM'11. .
PUB
 
[199]
2011 | Journal Article | PUB-ID: 1993288
Arnonkijpanich B, Hasenfuss A, Hammer B (2011)
Local matrix adaptation in topographic neural maps.
Neurocomputing 74(4): 522-539.
PUB | DOI | WoS
 
[198]
2010 | Conference Paper | PUB-ID: 1993536
Hammer B, Hasenfuss A (2010)
Clustering very large dissimilarity data sets.
In: Artificial Neural Networks in Pattern Recognition (ANNPR 2010). Proceedings. Schwenker F, El Gayar N (Eds); Lecture Notes in Artificial Intelligence, 5998. Berlin: Springer: 259-273.
PUB | DOI
 
[197]
2010 | Conference Paper | PUB-ID: 1796018
Arnonkijpanich B, Hasenfuss A, Hammer B (2010)
Local matrix learning in clustering and applications for manifold visualization.
Neural Networks 23(4): 476-486.
PUB | DOI | WoS | PubMed | Europe PMC
 
[196]
2010 | Conference Paper | PUB-ID: 1993978
Schleif F-M, Villmann T, Hammer B, Schneider P, Biehl M (2010)
Generalized derivative based Kernelized learning vector quantization.
In: Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Fyfe C, Tino P, Charles D, Garcia-Osorio C, Yin H (Eds); Berlin u.a.: Springer: 21-28.
PUB | DOI
 
[195]
2010 | Journal Article | PUB-ID: 1993466
Gori M, Hammer B, Hitzler P, Palm G (2010)
Perspectives and challenges for recurrent neural network training.
Logic Journal of the IGPL 18(5): 617-619.
PUB | DOI | WoS
 
[194]
2010 | Journal Article | PUB-ID: 1795962
Schneider P, Bunte K, Stiekema H, Hammer B, Villmann T, Biehl M (2010)
Regularization in Matrix Relevance Learning.
IEEE Transactions on Neural Networks 21(5): 831-840.
PUB | DOI | WoS | PubMed | Europe PMC
 
[193]
2010 | Conference Paper | PUB-ID: 2276547
Mokbel B, Gisbrecht A, Hammer B (2010)
On the effect of clustering on quality assessment measures for dimensionality reduction.
In: NIPS workshop on Challenges of Data Visualization. .
PUB
 
[192]
2010 | Conference Paper | PUB-ID: 2276543
Gisbrecht A, Mokbel B, Hammer B (2010)
The Nystrom approximation for relational generative topographic mappings.
In: NIPS workshop on challenges of Data Visualization. .
PUB
 
[191]
2010 | Conference (Editor) | PUB-ID: 2276535
Hammer B, Hitzler P, Maass W, Toussaint M (Eds) (2010)
Learning paradigms in dynamic environments, 25.07.10-30.07.20.; 10302.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
PUB
 
[190]
2010 | Journal Article | PUB-ID: 1794373
Hammer B, Hasenfuss A (2010)
Topographic Mapping of Large Dissimilarity Data Sets.
Neural Computation 22(9): 2229-2284.
PUB | DOI | WoS | PubMed | Europe PMC
 
[189]
2010 | Conference Paper | PUB-ID: 1993448
Gisbrecht A, Hammer B (2010)
Relevance learning in generative topographic maps.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 387-392.
PUB
 
[188]
2010 | Journal Article | PUB-ID: 1929672
Witoelar AW, Ghosh A, de Vries JJG, Hammer B, Biehl M (2010)
Window-Based Example Selection in Learning Vector Quantization.
Neural Computing 22(11): 2924-2961.
PUB | DOI | WoS | PubMed | Europe PMC
 
[187]
2010 | Conference Paper | PUB-ID: 1993452
Gisbrecht A, Mokbel B, Hammer B (2010)
Relational Generative Topographic Map.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 277-282.
PUB
 
[186]
2010 | Conference Paper | PUB-ID: 1993457
Gisbrecht A, Mokbel B, Hasenfuss A, Hammer B (2010)
Visualizing Dissimilarity Data using generative topographic mapping.
In: KI'2010. Dillmann R, Beyerer J, Hanebeck UD, Schulz T (Eds); 227-237.
PUB
 
[185]
2010 | Journal Article | PUB-ID: 1993435
Geweniger T, Zülke D, Hammer B, Villmann T (2010)
Median fuzzy-c-means for clustering dissimilarity data.
Neurocomputing 73(7-9): 1109-1116.
PUB | DOI | WoS
 
[184]
2010 | Journal Article | PUB-ID: 1796189
Bunte K, Hammer B, Wismueller A, Biehl M (2010)
Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data.
Neurocomputing 73(7-9): 1074-1092.
PUB | DOI | WoS
 
[183]
2010 | Conference Paper | PUB-ID: 1993367
Bunte K, Hammer B, Villmann T, Biehl M, Wismüller A (2010)
Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization.
In: ESANN'10. Proceedings of the 18th European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: D side: 87-92.
PUB
 
[182]
2010 | Conference Paper | PUB-ID: 1993273
Arnonkijpanich B, Hammer B (2010)
Global Coordination based on Matrix Neural Gas for Dynamic Texture Synthesis.
In: ANNPR'2010. Lecture Notes in Artificial Intelligence, 5998. El Gayar N, Schwenker F (Eds); Springer: 84-95.
PUB
 
[181]
2010 | Journal Article | PUB-ID: 1796195
Schneider P, Biehl M, Hammer B (2010)
Hyperparameter learning in probabilistic prototype-based models.
Neurocomputing 73(7-9): 1117-1124.
PUB | DOI | WoS
 
[180]
2010 | Conference Paper | PUB-ID: 1994127
Villmann T, Haase S, Schleif F-M, Hammer B (2010)
Divergence Based Online Learning in Vector Quantization.
In: Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Rutkowski L, Scherer R, Tadeusiewicz R, Zadeh L, Zurada J (Eds); Berlin, Heidelberg: Springer: 479-486.
PUB | DOI
 
[179]
2010 | Conference Paper | PUB-ID: 1994227
Villmann T, Schleif F-M, Hammer B (2010)
Sparse representation of data.
In: ESANN'10. Verleysen M (Ed); D side: 225-234.
PUB
 
[178]
2010 | Conference Paper | PUB-ID: 1994138
Villmann T, Haase S, Schleif F-M, Hammer B, Biehl M (2010)
The Mathematics of Divergence Based Online Learning in Vector Quanitzation.
In: ANNPR'2010. El Gayar N, Schwenker F (Eds); Berlin, Heidelberg: Springer: 108-119.
PUB
 
[177]
2010 | Journal Article | PUB-ID: 1994034
Simmuteit S, Schleif F-M, Villmann T, Hammer B (2010)
Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints.
Knowledge and Information Systems 25(2): 327-343.
PUB | DOI | WoS
 
[176]
2009 | Conference Paper | PUB-ID: 1993679
Hammer B, Schrauwen B, Steil JJ (2009)
Recent advances in efficient learning of recurrent networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brugge: d-facto: 213-226.
PUB
 
[175]
2009 | Conference Paper | PUB-ID: 1993361
Bunte K, Hammer B, Biehl M (2009)
Nonlinear dimension reduction and visualization of labeled data.
In: International Conference on Computer Analysis of Images and Patterns. Jiang X, Petkov N (Eds); Lecture Notes in Computer Science, 5702, Berlin: Springer: 1162-1170.
PUB | DOI
 
[174]
2009 | Conference Paper | PUB-ID: 1993422
Geweniger T, Zühlke D, Hammer B, Villmann T (2009)
Fuzzy variant of affinity propagation in comparison to median fuzzy c-means.
In: Advances in Self-Organizing Maps. Principe JC, Miikkulainen R (Eds); 72-79.
PUB | DOI
 
[173]
2009 | Journal Article | PUB-ID: 1993984
Schleif F-M, Villmann T, Kostrzewa M, Hammer B, Gammerman A (2009)
Cancer Informatics by Prototype-networks in Mass Spectrometry.
Artificial Intelligence in Medicine 45(2-3): 215-228.
PUB | DOI | WoS | PubMed | Europe PMC
 
[172]
2009 | Conference Paper | PUB-ID: 1993835
Mokbel B, Hasenfuss A, Hammer B (2009)
Graph-based Representation of Symbolic Musical Data.
In: Graph-Based Representation in Pattern Recognition (GbRPR 2009). Lecture Notes in Computer Science, 5534. Torsello A, Escolano F, Brun L (Eds); Berlin: Springer: 42-51.
PUB | DOI
 
[171]
2009 | Report | PUB-ID: 1993316
Biehl M, Hammer B, Schleif F-M, Schneider P, Villmann T (2009)
Stationarity of Matrix Relevance Learning Vector Quantization. Machine Learning Reports.
Leipzig: Universität Leipzig.
PUB
 
[170]
2009 | Book Chapter | PUB-ID: 1994160
Villmann T, Hammer B, Biehl M (2009)
Some theoretical aspects of the neural gas vector quantizer.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Springer: 23-34.
PUB | DOI
 
[169]
2009 | Conference (Editor) | PUB-ID: 1994310
Biehl M, Hammer B, Hochreiter S, Kremer SC, Villmann T (Eds) (2009)
Similarity-based learning on structures.; 9081.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
PUB
 
[168]
2009 | Book (Editor) | PUB-ID: 1994316
Biehl M, Hammer B, Verleysen M, Villmann T (Eds) (2009)
Similarity Based Clustering. Springer Lecture Notes Artificial Intelligence, 5400.
Berlin, Heidelberg: Springer.
PUB | DOI
 
[167]
2009 | Journal Article | PUB-ID: 1993269
Alex N, Hasenfuss A, Hammer B (2009)
Patch Clustering for Massive Data Sets.
Neurocomputing 72(7-9): 1455-1469.
PUB | DOI | WoS
 
[166]
2009 | Conference Paper | PUB-ID: 1993356
Bunte K, Biehl M, Hammer B (2009)
Nonlinear discriminative data visualization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 65-70.
PUB
 
[165]
2009 | Book Chapter | PUB-ID: 1993326
Biehl M, Hammer B, Schneider P, Villmann T (2009)
Metric learning for prototype based classification.
In: Innovations in Neural Information – Paradigms and Applications. Bianchini M, Maggini M, Scarselli F (Eds); Studies in Computational Intelligence, 247, Berlin: Springer: 183-199.
PUB | DOI
 
[164]
2009 | Conference Paper | PUB-ID: 1993429
Geweniger T, Zühlke D, Hammer B, Villmann T (2009)
Median variant of fuzzy-c-means.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 523-528.
PUB
 
[163]
2009 | Book Chapter | PUB-ID: 1993555
Hammer B, Hasenfuss A, Rossi F (2009)
Median topographic maps for biological data sets.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Springer: 92-117.
PUB | DOI
 
[162]
2009 | Conference Paper | PUB-ID: 1993994
Schneider P, Biehl M, Hammer B (2009)
Hyperparameter Learning in robust soft LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 517-522.
PUB
 
[161]
2009 | Conference Paper | PUB-ID: 1994152
Villmann T, Hammer B (2009)
Functional principal component learning using Oja's method and Sobolev norms.
In: Advances in Self-Organizing Maps. Principe JC, Miikkulainen R (Eds); 325-333.
PUB
 
[160]
2009 | Conference Paper | PUB-ID: 1994305
Witolaer A, Biehl M, Hammer B (2009)
Equilibrium properties of offline LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 535-540.
PUB
 
[159]
2009 | Journal Article | PUB-ID: 1994008
Schneider P, Biehl M, Hammer B (2009)
Distance learning in discriminative vector quantization.
Neural Computation 21(10): 2942-2969.
PUB | DOI | WoS | PubMed | Europe PMC
 
[158]
2009 | Journal Article | PUB-ID: 1994004
Schneider P, Biehl M, Hammer B (2009)
Adaptive relevance matrices in learning vector quantization.
Neural Computation 21(12): 3532-3561.
PUB | DOI | WoS | PubMed | Europe PMC
 
[157]
2008 | Conference Paper | PUB-ID: 1994072
Strickert M, Schneider P, Keilwagen J, Villmann T, Biehl M, Hammer B (2008)
Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics.
In: Artificial Neural Networks in Pattern Recognition. Third IAPR Workshop. Proceedings. Prevost L, Marinai S, Schwenker F (Eds); Lecture Notes in Computer Science, 5064, Berlin: Springer: 78-89.
PUB | DOI
 
[156]
2008 | Journal Article | PUB-ID: 1994253
Villmann T, Schleif F-M, Kostrzewa M, Walch A, Hammer B (2008)
Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods.
Briefings in Bioinformatics 9(2): 129-143.
PUB | DOI | WoS | PubMed | Europe PMC
 
[155]
2008 | Journal Article | PUB-ID: 2017617
Villmann T, Hammer B, Schleif F-M, Hermann W, Cottrell M (2008)
Fuzzy Classification Using Information Theoretic Learning Vector Quantization.
Neurocomputing 71(16-18): 3070-3076.
PUB | DOI | WoS
 
[154]
2008 | Report | PUB-ID: 1993278
Arnonkijpanich B, Hammer B, Hasenfuss A (2008)
Local Matrix Adaptation in Topographic Neural Maps. IfI-08-07.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[153]
2008 | Conference Paper | PUB-ID: 1994281
Winkler T, Drieseberg J, Hasenfuß A, Hammer B, Hormann K (2008)
Thinning Mesh Animations.
In: Proceedings of Vision, Modeling, and Visualization 2008. Deussen O, Keim D, Saupe D (Eds); Konstanz, Germany: Aka: 149-158.
PUB
 
[152]
2008 | Conference Paper | PUB-ID: 1993776
Hasenfuss A, Boerger W, Hammer B (2008)
Topographic processing of very large text datasets.
In: Smart Systems Engineering: Computational Intelligence in Architecting Systes (ANNIE 2008). Daglie CH (Ed); ASME Press: 525-532.
PUB | DOI
 
[151]
2008 | Conference Paper | PUB-ID: 1993788
Hasenfuss A, Hammer B (2008)
Single Pass Clustering and Classification of Large Dissimilarity Datasets.
In: Artificial Intelligence and Pattern Recognition. Prasad B, Sinha P, Ram A, Kerre EE (Eds); ISRST: 219-223.
PUB
 
[150]
2008 | Conference Paper | PUB-ID: 1994089
Strickert M, Sreenivasulu N, Villmann T, Hammer B (2008)
Robust Centroid-Based Clustering using Derivatives of Pearson Correlation.
In: BIOSIGNALS (2). Encarnação P, Veloso A (Eds); INSTICC - Institute for Systems and Technologies of Information, Control and Communication: 197-203.
PUB
 
[149]
2008 | Conference (Editor) | PUB-ID: 1994329
de Raedt L, Hammer B, Hitzler P, Maass W (Eds) (2008)
Recurrent Neural Networks - Models, Capacities, and Applications.; 8041.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
PUB
 
[148]
2008 | Journal Article | PUB-ID: 1993966
Schleif F-M, Villmann T, Hammer B (2008)
Prototype based Fuzzy Classification in Clinical Proteomics.
International Journal of Approximate Reasoning 47(1): 4-16.
PUB | DOI | WoS
 
[147]
2008 | Book Chapter | PUB-ID: 1993939
Schleif F-M, Villmann T, Hammer B (2008)
Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics.
In: Encyclopedia of Artificial Intelligence. Dopico JR-n R-al, Dorado J, Pazos A (Eds); IGI Global: 1337-1342.
PUB
 
[146]
2008 | Conference Paper | PUB-ID: 1993804
Hasenfuss A, Hammer B, Rossi F (2008)
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets.
In: Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064. Prevost L, Marinai S, Schwenker F (Eds); Berlin: Springer: 1-12.
PUB | DOI
 
[145]
2008 | Conference Paper | PUB-ID: 1993261
Alex N, Hammer B (2008)
Parallelizing single pass patch clustering.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere, Belgium: d-side publications: 227-232.
PUB
 
[144]
2008 | Conference Paper | PUB-ID: 1993282
Arnonkijpanich B, Hammer B, Hasenfuss A, Lursinsap C (2008)
Matrix Learning for Topographic Neural Maps.
In: ICANN (1). Lecture Notes in Computer Science, 5163. Kurková V, Neruda R, Koutn'ık J (Eds); Berlin: Springer: 572-582.
PUB
 
[143]
2008 | Report | PUB-ID: 1994012
Schneider P, Biehl M, Hammer B (2008)
Matrix Adaptation in Discriminative Vector Quantization. IfI Technical Report Seriess.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[142]
2008 | Conference Paper | PUB-ID: 1993798
Hasenfuss A, Hammer B, Geweniger T, Villmann T (2008)
Magnification Control in Relational Neural Gas.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 325-330.
PUB
 
[141]
2008 | Journal Article | PUB-ID: 1994290
Witoelar A, Biehl M, Ghosh A, Hammer B (2008)
Learning dynamics and robustness of vector quantization and neural gas.
Neurocomputing 71(7-9): 1210-1219.
PUB | DOI | WoS
 
[140]
2008 | Report | PUB-ID: 1993379
Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2008)
Discriminative Visualization by Limited Rank Matrix Learning. Machine Learning Reports.
Leipzig: Universität Leipzig.
PUB
 
[139]
2008 | Conference Paper | PUB-ID: 2001836
Geweniger T, Schleif F-M, Hasenfuss A, Hammer B, Villmann T (2008)
Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity.
In: ICONIP 2008. Köppen M, Kasabov NK, Coghill GG (Eds); Berlin, Heidelberg: Springer: 61-69.
PUB | DOI
 
[138]
2008 | Book Chapter | PUB-ID: 1993900
Schleif F-M, Hammer B, Villmann T (2008)
Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers.
In: Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Van de Werff M, Delder A, Tollenaar R (Eds); Berlin: Springer: 141-167.
PUB | DOI
 
[137]
2007 | Book Chapter | PUB-ID: 1994102
Tino P, Hammer B, Boden M (2007)
Markovian Bias of Neural-based Architectures With Feedback Connections.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 95-134.
PUB | DOI
 
[136]
2007 | Book Chapter | PUB-ID: 1993630
Hammer B, Micheli A, Sperduti A (2007)
Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 67-94.
PUB | DOI
 
[135]
2007 | Conference Paper | PUB-ID: 1993563
Hammer B, Hasenfuss A, Rossi F, Strickert M (2007)
Topographic Processing of Relational Data.
In: Proceedings of 6th International Workshop on Self-Organizing Maps. .
PUB
 
[134]
2007 | Conference Paper | PUB-ID: 1993907
Schleif F-M, Hammer B, Villmann T (2007)
Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg: Springer: 1036-1044.
PUB | DOI
 
[133]
2007 | Conference Paper | PUB-ID: 1993265
Alex N, Hammer B, Klawonn F (2007)
Single pass clustering for large data sets.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). .
PUB
 
[132]
2007 | Conference (Editor) | PUB-ID: 1994321
Biehl M, Hammer B, Verleysen M, Villmann T (Eds) (2007)
Similarity-based Clustering and its Application to Medicine and Biology.; 7131.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
PUB
 
[131]
2007 | Conference Paper | PUB-ID: 1993999
Schneider P, Biehl M, Hammer B (2007)
Relevance matrices in LVQ.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 37-42.
PUB
 
[130]
2007 | Conference Paper | PUB-ID: 1993782
Hasenfuss A, Hammer B (2007)
Relational topographic maps.
In: Advances in Intelligent Data Analysis VII, Proceedings of the 7th International Symposium on Intelligent Data Analysis. Berthold MR, Shawe-Taylor J, Lavrac N (Eds);4723. Berlin: Springer: 93-105.
PUB | DOI
 
[129]
2007 | Report | PUB-ID: 1993533
Hammer B, Hasenfuss A (2007)
Relational topographic Maps. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[128]
2007 | Conference Paper | PUB-ID: 1993541
Hammer B, Hasenfuss A (2007)
Relational Neural Gas.
In: KI 2007: Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence, 4667. Hertzberg J, Beetz M, Englert R (Eds); Berlin: Springer: 190-204.
PUB | DOI
 
[127]
2007 | Book (Editor) | PUB-ID: 1994326
Hammer B, Hitzler P (Eds) (2007)
Perspectives of Neural-Symbolic Integration. Studies in Computational Intelligence, 77.
Berlin: Springer.
PUB | DOI
 
[126]
2007 | Conference Paper | PUB-ID: 1994299
Witolaer A, Biehl M, Ghosh A, Hammer B (2007)
On the dynamics of vector quantization and neural gas.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 127-132.
PUB
 
[125]
2007 | Report | PUB-ID: 1993831
Melato M, Hammer B, Hormann K (2007)
Neural Gas for Surface Reconstruction. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[124]
2007 | Conference Paper | PUB-ID: 1993820
Hasenfuss A, Hammer B, Schleif F-M, Villmann T (2007)
Neural gas clustering for sparse proximity data.
In: Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg, Germany: Springer: 539-546.
PUB
 
[123]
2007 | Conference Paper | PUB-ID: 1993811
Hasenfuss A, Hammer B, Schleif F-M, Villmann T (2007)
Neural gas clustering for dissimilarity data with continuous prototypes.
In: Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 539-546.
PUB | DOI
 
[122]
2007 | Journal Article | PUB-ID: 1993911
Schleif F-M, Hammer B, Villmann T (2007)
Margin based Active Learning for LVQ Networks.
Neurocomputing 70(7-9): 1215-1224.
PUB | DOI | WoS
 
[121]
2007 | Journal Article | PUB-ID: 1993616
Hammer B, Hasenfuss A, Villmann T (2007)
Magnification control for batch neural gas.
Neurocomputing 70(7-9): 1225-1234.
PUB | DOI | WoS
 
[120]
2007 | Conference Paper | PUB-ID: 1994295
Witoelar A, Biehl M, Hammer B (2007)
Learning Vector Quantization: generalization ability and dynamics of competing prototypes.
In: Proceedings of 6th International Workshop on Self-Organizing Maps. .
PUB
 
[119]
2007 | Conference Paper | PUB-ID: 1993547
Hammer B, Hasenfuss A, Schleif F-M, Villmann T, Strickert M, Seiffert U (2007)
Intuitive Clustering of Biological Data.
In: Proceedings of International Joint Conference on Neural Networks. IEEE: 1877-1882.
PUB | DOI
 
[118]
2007 | Report | PUB-ID: 1993334
Blazewicz J, Ecker K, Hammer B (2007)
ICOLE-2007, German-Polish Workshop on Computational Biology, Scheduling and Machine Learning. Lessach, Austria, 27.05.-02.06.2007.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[117]
2007 | Conference Paper | PUB-ID: 1993746
Hammer B, Villmann T (2007)
How to process uncertainty in machine learning.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 79-90.
PUB
 
[116]
2007 | Conference Paper | PUB-ID: 1994258
Villmann T, Schleif F-M, Merenyi E, Hammer B (2007)
Fuzzy Labeled Self Organizing Map for Clasification of Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 556-563.
PUB | DOI
 
[115]
2007 | Journal Article | PUB-ID: 1993297
Biehl M, Ghosh A, Hammer B (2007)
Dynamics and generalization ability of LVQ algorithms.
Journal of Machine Learning Research 8: 323-360.
PUB
 
[114]
2007 | Conference Paper | PUB-ID: 1994267
Villmann T, Schleif F-M, Merenyi E, Strickert M, Hammer B (2007)
Class imaging of hyperspectral satellite remote sensing data using FLSOM.
In: Proceedings of 6th International Workshop on Self-Organizing Maps. .
PUB
 
[113]
2007 | Conference Paper | PUB-ID: 1993970
Schleif F-M, Villmann T, Hammer B (2007)
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps.
In: Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Masulli F, Mitra S, Pasi G (Eds); Berlin, Heidelberg: Springer: 563-570.
PUB | DOI
 
[112]
2007 | Report | PUB-ID: 1993922
Schleif F-M, Hasenfuss A, Hammer B (2007)
Aggregation of multiple peak lists by use of an improved neural gas network.
Leipzig: Universität Leipzig.
PUB
 
[111]
2007 | Conference Paper | PUB-ID: 1994016
Schneider P, Biehl M, Schleif F-M, Hammer B (2007)
Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data.
In: Proceedings of 6th International Workshop on Self-Organizing Maps. .
PUB
 
[110]
2007 | Conference Paper | PUB-ID: 1993848
Rossi F, Hasenfuß A, Hammer B (2007)
Accelerating Relational Clustering Algorithms With Sparse Prototype Representation.
In: Proceedings of 6th International Workshop on Self-Organizing Maps. .
PUB
 
[109]
2006 | Conference Paper | PUB-ID: 1994184
Villmann T, Hammer B, Schleif F-M, Geweniger T, Fischer T, Cottrell M (2006)
Prototype based classification using information theoretic learning.
In: Neural Information Processing, 13th International Conference. Proceedings. King I, Wang J, Chan L, Wang DLL (Eds); Lecture Notes in Computer Science, 4233, Part II. Berlin: Springer: 40-49.
PUB
 
[108]
2006 | Conference Paper | PUB-ID: 1993594
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median clustering.
In: Smart systems engineering : infra-structure systems engineering, bio-informatics and computational biology and evolutionary computation : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2006). Dagli CH (Ed); ASME Press series on intelligent engineering systems through artificial neural networks, 16, New York, NY: ASME Press: 623-632.
PUB
 
[107]
2006 | Journal Article | PUB-ID: 1993391
Cottrell M, Hammer B, Hasenfuss A, Villmann T (2006)
Batch and Median Neural Gas.
Neural Networks 19(6-7): 762-771.
PUB | DOI | WoS | PubMed | Europe PMC
 
[106]
2006 | Journal Article | PUB-ID: 1994237
Villmann T, Schleif F-M, Hammer B (2006)
Comparison of relevance learning vector quantization with other metric adaptive classification methods.
Neural Networks 19(5): 610-622.
PUB | DOI | WoS | PubMed | Europe PMC
 
[105]
2006 | Conference Paper | PUB-ID: 2017225
Hammer B, Villmann T, Schleif F-M, Albani C, Hermann W (2006)
Learning vector quantization classification with local relevance determination for medical data.
In: Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Rutkowski L, Tadeusiewicz R, Zadeh LA, Zurada J (Eds); Berlin, Heidelberg: Springer: 603-612.
PUB | DOI
 
[104]
2006 | Conference Paper | PUB-ID: 1993568
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median neural gas.
In: Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. Dagli C, Buczak A, Enke D, Embrechts A, Ersoy O (Eds); ASME Press: 623-633.
PUB
 
[103]
2006 | Report | PUB-ID: 1993584
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median clustering. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[102]
2006 | Conference Paper | PUB-ID: 1993578
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised Batch Neural Gas.
In: Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR). Schwenker F (Ed); Berlin: Springer Verlag: 33-45.
PUB | DOI
 
[101]
2006 | Journal Article | PUB-ID: 1994241
Villmann T, Schleif F-M, Hammer B (2006)
Prototype-based fuzzy classification with local relevance for proteomics.
Neurocomputing 69(16-18): 2425-2428.
PUB | DOI | WoS
 
[100]
2006 | Conference Paper | PUB-ID: 1994201
Villmann T, Hammer B, Seiffert U (2006)
Perspectives of Self-adapted Self-organizing Clustering in Organic Computing.
In: Biologically Inspired Approaches to Advanced Information Technology, Second International Workshop. Proceedings. Lecture Notes in Computer Science, 3853. Ijspeert AJ, Masuzawa T, Kusumoto S (Eds); Berlin: Springer: 141-159.
PUB | DOI
 
[99]
2006 | Journal Article | PUB-ID: 1993440
Ghosh A, Biehl M, Hammer B (2006)
Performance analysis of LVQ algorithms: a statistical physics approach.
Neural Networks 19(6-7): 817-829.
PUB | DOI | WoS | PubMed | Europe PMC
 
[98]
2006 | Conference Paper | PUB-ID: 1993659
Hammer B, Neubauer N (2006)
On the capacity of unsupervised recursive neural networks for symbol processing.
In: Workshop proceedings of NeSy'06. d'Avila Garcez A, Hitzler P, Tamburrini G (Eds);.
PUB
 
[97]
2006 | Conference Paper | PUB-ID: 1994028
Seiffert U, Hammer B, Kaski S, Villmann T (2006)
Neural Networks and Machine Learning in Bioinformatics - Theory and Applications.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 521-532.
PUB
 
[96]
2006 | Report | PUB-ID: 1993322
Biehl M, Hammer B, Schneider P (2006)
Matrix Learning in Learning Vector Quantization.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[95]
2006 | Conference Paper | PUB-ID: 1993895
Schleif F-M, Hammer B, Villmann T (2006)
Margin based Active Learning for LVQ Networks.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 539-544.
PUB
 
[94]
2006 | Conference Paper | PUB-ID: 1993611
Hammer B, Hasenfuss A, Villmann T (2006)
Magnification Control for Batch Neural Gas.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 7-12.
PUB
 
[93]
2006 | Conference Paper | PUB-ID: 1993889
Schleif F-M, Elssner T, Kostrzewa M, Villmann T, Hammer B (2006)
Machine Learning and Soft-Computing in Bioinformatics. A Short Journey.
In: Proc. of FLINS 2006. World Scientific Press: 541-548.
PUB
 
[92]
2006 | Journal Article | PUB-ID: 1993301
Biehl M, Ghosh A, Hammer B (2006)
Learning vector quantization: The dynamics of winner-takes-all algorithms.
Neurocomputing 69(7-9): 660-670.
PUB | DOI | WoS
 
[91]
2006 | Journal Article | PUB-ID: 1994082
Strickert M, Seiffert U, Sreenivasulu N, Weschke W, Villmann T, Hammer B (2006)
Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis.
Neurocomputing 69(7-9): 651-659.
PUB | DOI | WoS
 
[90]
2006 | Conference Paper | PUB-ID: 1994273
Villmann T, Seiffert U, Schleif F-M, Brüß C, Geweniger T, Hammer B (2006)
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes.
In: Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Schwenker F (Ed); Berlin: Springer: 46-56.
PUB | DOI
 
[89]
2006 | Journal Article | PUB-ID: 1994195
Villmann T, Hammer B, Schleif F-M, Geweniger T, Herrmann W (2006)
Fuzzy Classification by Fuzzy Labeled Neural Gas.
Neural Networks 19(6-7): 772-779.
PUB | DOI | WoS | PubMed | Europe PMC
 
[88]
2006 | Journal Article | PUB-ID: 1993762
Hammer B, Villmann T (2006)
Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern.
Künstliche Intelligenz 3(6): 5-11.
PUB
 
[87]
2006 | Conference Paper | PUB-ID: 1993878
Schleif F-M, Elssner T, Kostrzewa M, Villmann T, Hammer B (2006)
Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps.
In: 19th IEEE International Symposium on Computer- based Medical Systems. Lee DJ, Nutter B, Antani S, Mitra S, Archibald J (Eds); Los Alamitos: IEEE Computer Society Press: 919-924.
PUB | DOI
 
[86]
2005 | Journal Article | PUB-ID: 1994063
Strickert M, Hammer B, Blohm S (2005)
Unsupervised recursive sequences processing.
Neurocomputing 63: 69-97.
PUB | DOI | WoS
 
[85]
2005 | Journal Article | PUB-ID: 1993641
Hammer B, Micheli A, Sperduti A (2005)
Universal approximation capability of cascade correlation for structures.
Neural Computation 17(5): 1109-1159.
PUB | DOI | WoS
 
[84]
2005 | Conference Paper | PUB-ID: 1993305
Biehl M, Gosh A, Hammer B (2005)
The dynamics of Learning Vector Quantization.
In: ESANN'05. Verleysen M (Ed); Evere: d-side publishing: 13-18.
PUB
 
[83]
2005 | Journal Article | PUB-ID: 1993721
Hammer B, Strickert M, Villmann T (2005)
Supervised neural gas with general similarity measure.
Neural Processing Letters 21(1): 21-44.
PUB | DOI | WoS
 
[82]
2005 | Journal Article | PUB-ID: 1993671
Hammer B, Saunders C, Sperduti A (2005)
Special issue on neural networks and kernel methods for structured domains.
Neural Networks 18(8): 1015-1018.
PUB | DOI | WoS | PubMed | Europe PMC
 
[81]
2005 | Conference Paper | PUB-ID: 1993624
Hammer B, Micheli A, Neubauer N, Sperduti A, Strickert M (2005)
Self Organizing Maps for Time Series.
In: Proceedings of WSOM 2005. 115-122.
PUB
 
[80]
2005 | Conference Paper | PUB-ID: 1993665
Hammer B, Rechtien A, Strickert M, Villmann V (2005)
Relevance learning for mental disease classification.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 139-144.
PUB
 
[79]
2005 | Conference Paper | PUB-ID: 1994118
Tluk von Toschanowitz K, Hammer B, Ritter H (2005)
Relevance determination in reinforcement learning.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 369-374.
PUB
 
[78]
2005 | Book Chapter | PUB-ID: 1993710
Hammer B, Strickert M, Villmann T (2005)
Prototype based recognition of splice sites.
In: Bioinformatics using computational intelligence paradigms. Seiffert U, Jain LC, Schweitzer P (Eds); Berlin: Springer: 25-55.
PUB
 
[77]
2005 | Report | PUB-ID: 1993675
Hammer B, Schleif F-M, Villmann T (2005)
On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[76]
2005 | Journal Article | PUB-ID: 1993717
Hammer B, Strickert M, Villmann T (2005)
On the generalization ability of GRLVQ networks.
Neural Processing Letters 21(2): 109-120.
PUB | DOI | WoS
 
[75]
2005 | Journal Article | PUB-ID: 1993406
DasGupta B, Hammer B (2005)
On approximate learning by multi-layered feedforward circuits.
Theoretical Computer Science 348(1): 95-127.
PUB | DOI | WoS
 
[74]
2005 | Journal Article | PUB-ID: 1993396
Cottrell M, Hammer B, Villmann T (2005)
New Aspects in Neurocomputing.
Neurocomputing 63: 1-3.
PUB | DOI | WoS
 
[73]
2005 | Journal Article | PUB-ID: 1994057
Strickert M, Hammer B (2005)
Merge SOM for temporal data.
Neurocomputing 64: 39-71.
PUB | DOI | WoS
 
[72]
2005 | Conference Paper | PUB-ID: 1993974
Schleif F-M, Villmann T, Hammer B (2005)
Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data.
In: Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Bloch I, Petrosino A, Tettamanzi AGB (Eds); Berlin, Heidelberg: Springer: 290-296.
PUB | DOI
 
[71]
2005 | Journal Article | PUB-ID: 1993416
Gersmann K, Hammer B (2005)
Improving iterative repair strategies for scheduling with the SVM.
Neurocomputing 63: 271-292.
PUB | DOI | WoS
 
[70]
2005 | Conference Paper | PUB-ID: 1994249
Villmann T, Schleif F-M, Hammer B (2005)
Fuzzy labeled soft nearest neighbor classification with relevance learning.
In: Proceedings of the International Conference of Machine Learning Applications. Wani MA, Cios KJ, Hafeez K (Eds); Los Angeles: IEEE Press: 11-15.
PUB
 
[69]
2005 | Conference Paper | PUB-ID: 1994172
Villmann T, Hammer B, Schleif F-M, Geweniger T (2005)
Fuzzy Labeled Neural GAS for Fuzzy Classification.
In: Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Cottrell M (Ed); Paris, France: University Paris-1-Pantheon-Sorbonne: 283-290.
PUB
 
[68]
2005 | Conference Paper | PUB-ID: 1994219
Villmann T, Schleif F-M, Hammer B (2005)
Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization.
In: International Workshop on Integrative Bioinformatics. .
PUB
 
[67]
2005 | Conference Paper | PUB-ID: 1993444
Ghosh A, Biehl M, Hammer B (2005)
Dynamical Analysis of LVQ type learning rules.
In: Proceedings of WSOM. 578-594.
PUB
 
[66]
2005 | Conference Paper | PUB-ID: 1993750
Hammer B, Villmann T (2005)
Classification using non standard metrics.
In: ESANN'05. Verleysen M (Ed); Brussels: d-side publishing: 303-316.
PUB
 
[65]
2005 | Conference Paper | PUB-ID: 1993386
Cottrell M, Hammer B, Hasenfuss A, Villmann T (2005)
Batch NG.
In: Proceedings of WSOM 2005. 275-282.
PUB
 
[64]
2004 | Conference Paper | PUB-ID: 1993870
Schleif F-M, Clauss U, Villmann T, Hammer B (2004)
Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data.
In: Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Wani MA, Cios KJ, Hafeez K (Eds); Los Alamitos, CA, USA: IEEE Press: 374-379.
PUB
 
[63]
2004 | Conference Paper | PUB-ID: 1994049
Strickert M, Hammer B (2004)
Self-organizing context learning.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-side publications: 39-44.
PUB
 
[62]
2004 | Conference Paper | PUB-ID: 1993702
Hammer B, Strickert M, Villmann T (2004)
Relevance LVQ versus SVM.
In: Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070. Rutkowski L, Siekmann J, Tadeusiewicz R, Zadeh LA (Eds); Berlin: Springer: 592-597.
PUB
 
[61]
2004 | Journal Article | PUB-ID: 1993649
Hammer B, Micheli A, Sperduti A, Strickert M (2004)
Recursive self-organizing network models.
Neural Networks 17(8-9): 1061-1085.
PUB | DOI | WoS | PubMed | Europe PMC
 
[60]
2004 | Conference Paper | PUB-ID: 1994099
Tino P, Hammer B (2004)
On early stages of learning in connectionist models with feedback connections.
In: Compositional Connectionism in Cognitive Science. .
PUB
 
[59]
2004 | Conference Paper | PUB-ID: 1993620
Hammer B, Jain BJ (2004)
Neural methods for non-standard data.
In: European Symposium on Artificial Neural Networks'2004. Verleysen M (Ed); D-side publications: 281-292.
PUB
 
[58]
2004 | Conference Paper | PUB-ID: 1994168
Villmann T, Hammer B, Schleif F-M (2004)
Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection.
In: Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 592-597.
PUB
 
[57]
2004 | Conference Paper | PUB-ID: 1994212
Villmann T, Schleif F-M, Hammer B (2004)
Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag.
PUB
 
[56]
2004 | Conference Paper | PUB-ID: 1994111
Tluk von Toschanowitz K, Hammer B, Ritter H (2004)
Mapping the Design Space of Reinforcement Learning Problems - a Case Study.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Gross H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 251-261.
PUB
 
[55]
2004 | Conference Paper | PUB-ID: 1993419
Gersmann K, Hammer B (2004)
A reinforcement learning algorithm to improve scheduling search heuristics with the SVM.
In: IJCNN. .
PUB
 
[54]
2004 | Report | PUB-ID: 1993732
Hammer B, Tino P, Micheli A (2004)
A mathematical characterization of the architectural bias of recursive models. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[53]
2004 | Journal Article | PUB-ID: 1993654
Hammer B, Micheli A, Sperduti A, Strickert M (2004)
A general framework for unsupervised processing of structured data.
Neurocomputing 57: 3-35.
PUB | DOI | WoS
 
[52]
2003 | Journal Article | PUB-ID: 1994208
Villmann T, Merényi E, Hammer B (2003)
Neural maps in remote sensing image analysis.
Neural Networks 16(3-4): 389-403.
PUB
 
[51]
2003 | Conference Paper | PUB-ID: 1994053
Strickert M, Hammer B (2003)
Unsupervised recursive sequence processing.
In: 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); D-side publication: 27-32.
PUB
 
[50]
2003 | Conference Paper | PUB-ID: 1994223
Villmann T, Schleif F-M, Hammer B (2003)
Supervised Neural Gas and Relevance Learning in Learning Vector Quantization.
In: Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Yamakawa T (Ed); Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology: 47-52.
PUB
 
[49]
2003 | Journal Article | PUB-ID: 1993736
Hammer B, Tiño P (2003)
Recurrent Neural Networks with Small Weights Implement Definite Memory Machines.
Neural Computation 15(8): 1897-1929.
PUB
 
[48]
2003 | Book Chapter | PUB-ID: 1993487
Hammer B (2003)
Perspectives on learning symbolic data with connectionistic systems.
In: Adaptivity and Learning. Kühn R, Menzel R, Menzel W, Ratsch U, Richter MM, Stamatescu I (Eds); Berlin: Springer: 141-160.
PUB
 
[47]
2003 | Report | PUB-ID: 1993725
Hammer B, Strickert M, Villmann T (2003)
On the generalization ability of GRLVQ. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[46]
2003 | Conference Paper | PUB-ID: 1994060
Strickert M, Hammer B (2003)
Neural Gas for Sequences.
In: WSOM'03. 53-57.
PUB
 
[45]
2003 | Conference Paper | PUB-ID: 1993338
Bojer T, Hammer B, Koeers C (2003)
Monitoring technical systems with prototype based clustering.
In: ESANN 2003, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); Evere: D-side publication: 433-439.
PUB
 
[44]
2003 | Report | PUB-ID: 1994157
Villmann T, Hammer B (2003)
Metric adaptation and relevance learning in learning vector quantization. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[43]
2003 | Conference Paper | PUB-ID: 1993754
Hammer B, Villmann T (2003)
Mathematical Aspects of Neural Networks.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2003). Verleysen M (Ed); Brussels, Belgium: d-side: 59-72.
PUB
 
[42]
2003 | Conference Paper | PUB-ID: 1993412
Gersmann K, Hammer B (2003)
Improving iterative repair strategies for scheduling with the SVM.
In: ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); Evere: D-side publication: 235-240.
PUB
 
[41]
2003 | Conference Paper | PUB-ID: 1993349
Bojer T, Hammer B, Strickert M, Villmann T (2003)
Determining Relevant Input Dimensions for the Self-Organizing Map.
In: Neural Networks and Soft Computing (Proc. ICNNSC 2002). Rutkowski L, Kacprzyk J (Eds); Physica-Verlag: 388-393.
PUB
 
[40]
2003 | Journal Article | PUB-ID: 1994108
Tiño P, Hammer B (2003)
Architectural Bias in Recurrent Neural Networks: Fractal Analysis.
Neural Computation 15(8): 1931-1957.
PUB
 
[39]
2003 | Journal Article | PUB-ID: 1993530
Hammer B, Gersmann K (2003)
A Note on the Universal Approximation Capability of Support Vector Machines.
Neural Processing Letters 17(1): 43-53.
PUB
 
[38]
2003 | Report | PUB-ID: 1993645
Hammer B, Micheli a., Sperduti A (2003)
A general framework for self-organizing structure processing neural networks.
Pisa: Universita di Pisa, Dipartimento die Informatica.
PUB
 
[37]
2002 | Conference Paper | PUB-ID: 1993688
Hammer B, Steil JJ (2002)
Perspectives on Learning with Recurrent Neural Networks.
In: Proc. European Symposium Artificial Neural Networks. Verleysen M (Ed); D-side publication: 357-368.
PUB
 
[36]
2002 | Journal Article | PUB-ID: 1993765
Hammer B, Villmann T (2002)
Generalized Relevance Learning Vector Quantization.
Neural Networks 15(8-9): 1059-1068.
PUB
 
[35]
2002 | Conference Paper | PUB-ID: 1994146
Villmann T, Hammer B (2002)
Supervised Neural Gas for Learning Vector Quantization.
In: Proc. of the 5th German Workshop on Artificial Life. Polani D, Kim J, Martinetz T (Eds); Berlin: Akademische Verlagsgesellschaft - infix - IOS Press: 9-16.
PUB
 
[34]
2002 | Conference Paper | PUB-ID: 1993697
Hammer B, Strickert M, Villmann T (2002)
Rule Extraction from Self-Organizing Networks.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 877-883.
PUB
 
[33]
2002 | Journal Article | PUB-ID: 1993508
Hammer B (2002)
Recurrent neural networks for structured data – a unifying approach and its properties.
Cognitive Systems Research 3(2): 145-165.
PUB
 
[32]
2002 | Report | PUB-ID: 1993729
Hammer B, Tino P (2002)
Neural networks with small weights implement finite memory machines. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[31]
2002 | Conference Paper | PUB-ID: 1993692
Hammer B, Strickert M, Villmann T (2002)
Learning Vector Quantization for Multimodal Data.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 370-376.
PUB
 
[30]
2002 | Book Chapter | PUB-ID: 1993471
Hammer B (2002)
Compositionality in Neural Systems.
In: Handbook of Brain Theory and Neural Networks. Arbib M (Ed); 2nd. MIT Press: 244-248.
PUB
 
[29]
2002 | Conference Paper | PUB-ID: 1993758
Hammer B, Villmann T (2002)
Batch-GRLVQ.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2002). Verleysen M (Ed); Brussels, Belgium: d-side: 295-300.
PUB
 
[28]
2002 | Conference Paper | PUB-ID: 1994095
Tino P, Hammer B (2002)
Architectural bias in recurrent neural networks – fractal analysis.
In: Proc. International Conf. on Artificial Neural Networks. Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer: 370-376.
PUB
 
[27]
2002 | Conference Paper | PUB-ID: 1993636
Hammer B, Micheli A, Sperduti A (2002)
A general framework for unsupervised processing of structured data.
In: ESANN 2002, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); De-side publication: 389-394.
PUB
 
[26]
2001 | Conference Paper | PUB-ID: 1993343
Bojer T, Hammer B, Schunk D, Tluk von Toschanowitz K (2001)
Relevance determination in learning vector quantization.
In: ESANN'2001. Verleysen M (Ed); D-facto publications: 271-276.
PUB
 
[25]
2001 | Conference Paper | PUB-ID: 1993474
Hammer B (2001)
On the Generalization Ability of Recurrent Networks.
In: Artificial Neural Networks. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 731-736.
PUB
 
[24]
2001 | Conference Paper | PUB-ID: 1993768
Hammer B, Villmann T (2001)
Input Pruning for Neural Gas Architectures.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001). Brussels, Belgium: D facto publications: 283-288.
PUB
 
[23]
2001 | Conference Paper | PUB-ID: 1994042
Strickert M, Bojer T, Hammer B (2001)
Generalized Relevance LVQ for Time Series.
In: Artificial Neural Networks. International Conference. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 677-683.
PUB
 
[22]
2001 | Journal Article | PUB-ID: 1993510
Hammer B (2001)
Generalization Ability of Folding Networks.
IEEE Trans. Knowl. Data Eng. 13(2): 196-206.
PUB
 
[21]
2001 | Conference Paper | PUB-ID: 1993739
Hammer B, Villmann T (2001)
Estimating Relevant Input Dimensions for Self-Organizing Algorithms.
In: Advances in Self-Organising Maps. Allinson NM, Yin H, Allinson L, Slack J (Eds); London: Springer: 173-180.
PUB
 
[20]
2001 | Journal Article | PUB-ID: 1994123
Vidyasagar M, Balaji S, Hammer B (2001)
Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures.
System and Control Letters 42: 151-157.
PUB
 
[19]
2000 | Journal Article | PUB-ID: 1993512
Hammer B (2000)
On the approximation capability of recurrent neural networks.
Neurocomputing 31(1-4): 107-123.
PUB
 
[18]
2000 | Conference Paper | PUB-ID: 1993400
DasGupta B, Hammer B (2000)
On Approximate Learning by Multi-layered Feedforward Circuits.
In: Algorithmic Learning Theory, 11th International Conference. Proceedings. Lecture Notes in Computer Science, 1968. Arimura H, Jain S, Sharma A (Eds); Berlin: Springer: 264-278.
PUB
 
[17]
2000 | Conference Paper | PUB-ID: 1993495
Hammer B (2000)
Neural networks classifying symbolic data.
In: ICML workshop on attribute-value and relational learning: crossing the boundaries. de Raedt L, Kramer S (Eds); 61-65.
PUB
 
[16]
2000 | Conference Paper | PUB-ID: 1993499
Hammer B (2000)
Limitations of hybrid systems.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 213-218.
PUB
 
[15]
2000 | Book | PUB-ID: 1993514
Hammer B (2000)
Learning with Recurrent Neural Networks. Lecture Notes in Control and Information Sciences, 254.
Berlin: Springer.
PUB
 
[14]
2000 | Conference Paper | PUB-ID: 1993479
Hammer B (2000)
Approximation and generalization issues of recurrent networks dealing with structured data.
In: ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms. Frasconi P, Sperduti A, Gori M (Eds);.
PUB
 
[13]
1999 | Journal Article | PUB-ID: 1993516
Hammer B (1999)
On the learnability of recursive data.
Mathematics of Control, Signals and Systems 12: 62-79.
PUB
 
[12]
1999 | Report | PUB-ID: 1993409
DasGupta B, Hammer B (1999)
Hardness of approximation of the loading problem for multi-layered feedforward neural networks.
DIMACS Center, Rutgers University.
PUB
 
[11]
1999 | Conference Paper | PUB-ID: 1993502
Hammer B (1999)
Approximation capabilities of folding networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 33-38.
PUB
 
[10]
1998 | Conference Paper | PUB-ID: 1993505
Hammer B (1998)
Training a sigmoidal network is difficult.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 255-260.
PUB
 
[9]
1998 | Conference Paper | PUB-ID: 1993518
Hammer B (1998)
Some complexity results for perceptron networks.
In: International Conference on artificial Neural Networks. 639-644.
PUB
 
[8]
1998 | Conference Paper | PUB-ID: 1993484
Hammer B (1998)
On the Approximation Capability of Recurrent Neural Networks.
In: Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998). Heiss M (Ed); ICSC Academic Press: 512-518.
PUB
 
[7]
1997 | Report | PUB-ID: 1993524
Hammer B (1997)
On the generalization ability of simple recurrent neural networks. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[6]
1997 | Conference Paper | PUB-ID: 1993684
Hammer B, Sperschneider V (1997)
Neural networks can approximate mappings on structured objects.
In: International conference on Computational Intelligence and Neural Networks. Wang PP (Ed); 211-214.
PUB
 
[5]
1997 | Report | PUB-ID: 1993520
Hammer B (1997)
Learning recursive data is intractable. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[4]
1997 | Conference Paper | PUB-ID: 1993526
Hammer B (1997)
Generalization of Elman Networks.
In: Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327. Berlin: Springer: 409-414.
PUB
 
[3]
1997 | Report | PUB-ID: 1993522
Hammer B (1997)
A NP-hardness result for a sigmoidal 3-node neural network. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[2]
1996 | Report | PUB-ID: 1993528
Hammer B (1996)
Universal approximation of mappings on structured objects using the folding architecture. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
 
[1]
1996 | Book | PUB-ID: 1994039
Sperschneider V, Hammer B (1996)
Theoretische Informatik. Eine problemorientierte Einführung.
erlin: Springer.
PUB
 

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