129 Publikationen

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[129]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031
B. Mokbel, et al., “Metric learning for sequences in relational LVQ”, Neurocomputing, vol. 169, 2015, pp. 306-322.
PUB | PDF | DOI | Download (ext.) | WoS
 
[128]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2910619
F.-M. Schleif, T. Villmann, and X. Zhu, “High Dimensional Matrix Relevance Learning”, 2014 IEEE International Conference on Data Mining Workshop, Piscataway, NJ: IEEE, 2015.
PUB | DOI
 
[127]
2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2910885
F.-M. Schleif, A. Gisbrecht, and P. Tino, “Large Scale Indefinite Kernel Fisher Discriminant”, Similarity-Based Pattern Recognition. Similarity-Based Pattern Recognition : Third International Workshop, SIMBAD 2015, Proceedings, A. Feragen, M. Pelillo, and M. Loog, eds., Lecture Notes in Computer Science, vol. 9370, Cham: Springer International Publishing, 2015, pp.160-170.
PUB | DOI
 
[126]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772422
A. Gisbrecht and F.-M. Schleif, “Metric and non-metric proximity transformations at linear costs”, Neurocomputing, vol. 167, 2015, pp. 643-657.
PUB | DOI | WoS
 
[125]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2759763
F.-M. Schleif, X. Zhu, and B. Hammer, “Sparse conformal prediction for dissimilarity data”, Annals of Mathematics and Artificial Intelligence, vol. 74, 2015, pp. 95-116.
PUB | DOI | WoS
 
[124]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
D. Hofmann, et al., “Learning interpretable kernelized prototype-based models”, Neurocomputing, vol. 141, 2014, pp. 84-96.
PUB | DOI | Download (ext.) | WoS
 
[123]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730
B. Hammer, et al., “Learning vector quantization for (dis-)similarities”, NeuroComputing, vol. 131, 2014, pp. 43-51.
PUB | DOI | WoS
 
[122]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2690490
M. Strickert, et al., “Correlation-based embedding of pairwise score data”, Neurocomputing, vol. 141, 2014, pp. 97-109.
PUB | DOI | WoS
 
[121]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2672504
X. Zhu, F.-M. Schleif, and B. Hammer, “Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data”, Pattern Recognition Lettters, vol. 49, 2014, pp. 138-145.
PUB | DOI | WoS
 
[120]
2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2612731
A. Micheli, F.-M. Schleif, and P. Tino, “Novel approaches in machine learning and computational intelligence”, Neurocomputing, vol. 112, 2013, pp. 1-3.
PUB | DOI | WoS
 
[119]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625202
F.-M. Schleif, X. Zhu, and B. Hammer, “Sparse prototype representation by core sets”, IDEAL 2013, et.al Hujun Yin, ed., 2013.
PUB
 
[118]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615717
X. Zhu, F.-M. Schleif, and B. Hammer, “Secure Semi-supervised Vector Quantization for Dissimilarity Data”, IWANN (1), I. Rojas, G. Joya, and J. Cabestany, eds., Lecture Notes in Computer Science, vol. 7902, Springer, 2013, pp.347-356.
PUB | DOI
 
[117]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615724
F.-M. Schleif and A. Gisbrecht, “Data Analysis of (Non-)Metric Proximities at Linear Costs”, Proceedings of SIMBAD 2013, Springer, 2013, pp.59-74.
PUB | DOI
 
[116]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615701
X. Zhu, F.-M. Schleif, and B. Hammer, “Semi-Supervised Vector Quantization for proximity data”, Proceedings of ESANN 2013, 2013, pp.89-94.
PUB
 
[115]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625232
A. Gisbrecht, et al., “Linear Time Relational Prototype Based Learning”, Int. J. Neural Syst., vol. 22, 2012.
PUB | DOI | WoS | PubMed | Europe PMC
 
[114]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615745
K. Bunte, F.-M. Schleif, and M. Biehl, “Adaptive Learning for complex-valued data”, Proceedings of ESANN 2012, 2012, pp.387-392.
PUB
 
[113]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534898
M. Biehl, et al., “Large margin linear discriminative visualization by Matrix Relevance Learning”, IJCNN, 2012, pp.1-8.
PUB | DOI
 
[112]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615750
F.-M. Schleif, et al., “Fast approximated relational and kernel clustering”, Proceedings of ICPR 2012, IEEE, 2012, pp.1229-1232.
PUB
 
[111]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615756
F.-M. Schleif, X. Zhu, and B. Hammer, “Soft Competitive Learning for large data sets”, Proceedings of MCSD 2012, 2012, pp.141-151.
PUB | DOI
 
[110]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2534839
A. Gisbrecht, et al., “Linear Time Relational Prototype Based Learning”, Int. J. Neural Syst., vol. 22, 2012, pp. 1250021.
PUB | DOI | WoS | PubMed | Europe PMC
 
[109]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534877
F.-M. Schleif, et al., “Learning Relevant Time Points for Time-Series Data in the Life Sciences”, ICANN (2), Lecture Notes in Computer Science, vol. 7553, 2012, pp.531-539.
PUB | DOI
 
[108]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
K. Bunte, et al., “Limited Rank Matrix Learning, discriminative dimension reduction and visualization”, Neural Networks, vol. 26, 2012, pp. 159-173.
PUB | DOI | WoS | PubMed | Europe PMC
 
[107]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534888
F.-M. Schleif, X. Zhu, and B. Hammer, “A Conformal Classifier for Dissimilarity Data”, AIAI (2), 2012, pp.234-243.
PUB | DOI
 
[106]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534910
X. Zhu, F.-M. Schleif, and B. Hammer, “Patch Processing for Relational Learning Vector Quantization”, ISNN (1), 2012, pp.55-63.
PUB | DOI
 
[105]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534868
B. Hammer, et al., “White Box Classification of Dissimilarity Data”, HAIS (1), 2012, pp.309-321.
PUB | DOI | WoS
 
[104]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534905
F.-M. Schleif, A. Gisbrecht, and B. Hammer, “Relevance learning for short high-dimensional time series in the life sciences”, IJCNN, 2012, pp.1-8.
PUB | DOI
 
[103]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509852
X. Zhu, et al., “Approximation techniques for clustering dissimilarity data”, Neurocomputing, vol. 90, 2012, pp. 72-84.
PUB | DOI | WoS
 
[102]
2011 | Preprint | Veröffentlicht | PUB-ID: 2534994
F.-M. Schleif, A. Gisbrecht, and B. Hammer, “Supervised learning of short and high-dimensional temporal sequences for life science measurements”, 2011.
PUB | arXiv
 
[101]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276480
A. Gisbrecht, et al., “Linear time heuristics for topographic mapping of dissimilarity data”, 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, vol. 6936, Berlin, Heidelberg: Springer, 2011, pp.25-33.
PUB | DOI
 
[100]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276485
B. Hammer, et al., “Topographic Mapping of Dissimilarity Data”, WSOM'11, 2011.
PUB
 
[99]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276492
F.-M. Schleif, A. Gisbrecht, and B. Hammer, “Accelerating Kernel Neural Gas”, ICANN'2011, S. Kaski, et al., eds., 2011.
PUB
 
[98]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276644
U. Seiffert, F.-M. Schleif, and D. Zühlke, “Recent Trends in Computational Intelligence in Life Science”, Proceedings of ESANN 2011, 2011, pp.77-86.
PUB
 
[97]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276640
K. Bunte, F.-M. Schleif, and T. Villmann, “Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization”, Proceedings of ESANN 2011, Ciaco - i6doc.com, 2011, pp.29-34.
PUB
 
[96]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2290045
J.A. Lee, F.-M. Schleif, and T. Martinetz, “Advances in artificial neural networks, machine learning, and computational intelligence”, Neurocomputing, vol. 74, 2011, pp. 1299-1300.
PUB | DOI | WoS
 
[95]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2309980
F.-M. Schleif, et al., “Efficient Kernelized Prototype-based Classification”, International Journal of Neural Systems, vol. 21, 2011, pp. 443-457.
PUB | DOI | WoS | PubMed | Europe PMC
 
[94]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276522
A. Gisbrecht, et al., “Accelerating dissimilarity clustering for biomedical data analysis”, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2011, pp.pp.154-161.
PUB
 
[93]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276654
F.-M. Schleif, “Sparse Kernel Vector Quantization with Local Dependencies”, Proceedings of IJCNN 2011, 2011, pp.accepted.
PUB
 
[92]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992489
E. Mwebaze, et al., “Divergence based classification in Learning Vector Quantization”, Neurocomputing, vol. 74, 2011, pp. 1429-1435.
PUB | DOI | WoS
 
[91]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2094556
F.-M. Schleif, et al., “Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications”, Bioinformatics, vol. 27, 2011, pp. 524-533.
PUB | DOI | WoS | PubMed | Europe PMC
 
[90]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276636
F.-M. Schleif, S. Simmuteit, and T. Villmann, “Hierarchical deconvolution of linear mixtures of high-dimensional mass spectra in micro-biology”, Proceedings of AIA 2011, 2011, pp.in press.
PUB
 
[89]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276648
P. Schneider, et al., “Multivariate class labeling in Robust Soft LVQ”, Proceedings of ESANN 2011, 2011, pp.17-22.
PUB
 
[88]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276626
S. Simmuteit, F.-M. Schleif, and T. Villmann, “Hierarchical evolving trees together with global and local learning for large data sets in MALDI imaging”, Proceedings of WCSB 2010, 2010, pp.103-106.
PUB
 
[87]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994127
T. Villmann, et al., “Divergence Based Online Learning in Vector Quantization”, Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113, L. Rutkowski, et al., eds., Berlin, Heidelberg: Springer, 2010, pp.479-486.
PUB | DOI
 
[86]
2010 | Konferenzbeitrag | Im Druck | PUB-ID: 1992498
E. Mwebaze, et al., “Divergence based Learning Vector Quantization”, Proceedings of ESANN 2010, In Press.
PUB
 
[85]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276630
F.-M. Schleif, et al., “Efficient identification and quantification of metabolites in 1-H NMR measurements by a novel data encoding approach”, Proceedings of WCSB 2010, 2010, pp.91-94.
PUB
 
[84]
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992441
C. Angulo, J.A. Lee, and F.-M. Schleif, “Advances in computational intelligence and learning”, NeuroComputing, vol. 73, 2010, pp. 1049-1050.
PUB | DOI | WoS
 
[83]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993978
F.-M. Schleif, et al., “Generalized derivative based Kernelized learning vector quantization”, Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings, C. Fyfe, et al., eds., Berlin u.a.: Springer, 2010, pp.21-28.
PUB | DOI
 
[82]
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994034
S. Simmuteit, et al., “Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints”, Knowledge and Information Systems, vol. 25, 2010, pp. 327-343.
PUB | DOI | WoS
 
[81]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992623
D. Zühlke, et al., “Learning vector quantization for heterogeneous structured data”, Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN) 2010, Evere, Belgium: d-side publications, 2010.
PUB
 
[80]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994138
T. Villmann, et al., “The Mathematics of Divergence Based Online Learning in Vector Quanitzation”, ANNPR'2010, N. El Gayar and F. Schwenker, eds., Berlin, Heidelberg: Springer, 2010, pp.108-119.
PUB
 
[79]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994227
T. Villmann, F.-M. Schleif, and B. Hammer, “Sparse representation of data”, ESANN'10, M. Verleysen, ed., D side, 2010, pp.225-234.
PUB
 
[78]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993984
F.-M. Schleif, et al., “Cancer Informatics by Prototype-networks in Mass Spectrometry”, Artificial Intelligence in Medicine, vol. 45, 2009, pp. 215-228.
PUB | DOI | WoS | PubMed | Europe PMC
 
[77]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992551
F.-M. Schleif and T. Villmann, “Neural Maps and Learning Vector Quantization - Theory and Applications”, Proceedings of the ESANN 2009. European Symposium on Artificial Neural Networks. Advances in Computational Intelligence and Learning, Evere, Belgium: d-side publications, 2009, pp.509-516.
PUB
 
[76]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992570
S. Simmuteit, et al., “Hierarchical PCA using Tree-SOM for the Identification of Bacteria”, Advances in Self-Organizing Maps. Proceedings of the 7th International Workshop on Self Organizing Maps WSOM 2009. LNCS, 5629, J.C. Príncipe and R. Miikkulainen, eds., Berlin: Springer, 2009, pp.272-280.
PUB | DOI
 
[75]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992575
S. Simmuteit, et al., “Deconvolution and Identification of Mass Spectra from mixed and pure colonies of bacteria”, ICOLE 2009, J. Blazewicz, K. Ecker, and B. Hammer, eds., IfI-09-12, Clausthal-Zellerfeld, Germany: Technical University of Clausthal, 2009, pp.104-112.
PUB
 
[74]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992607
T. Villmann and F.-M. Schleif, “Functional Vector Quantization by Neural Maps”, Proceedings of Whispers 2009, 2009.
PUB | DOI
 
[73]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994067
M. Strickert, et al., “Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data”, Similarity-based Clustering, M. Biehl, et al., eds., LNAI, 5400, Berlin: Springer, 2009, pp.70-91.
PUB | DOI
 
[72]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992547
F.-M. Schleif, M. Biehl, and A. Vellido, “Advances in machine learning and computational intelligence”, NeuroComputing, vol. 72, 2009, pp. 1377-1378.
PUB | DOI | WoS
 
[71]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992580
M. Strickert, et al., “Matrix metric adaptation for improved linear discriminant analysis of biomedical data”, Bio-Inspired Systems: Computational and Ambient Intelligence, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. LNCS, 5517, J. Cabestany, et al., eds., vol. Part 1, Berlin: Springer, 2009, pp.933-940.
PUB | DOI
 
[70]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992526
F.-M. Schleif, T. Villmann, and M. Ongyerth, “Supervised data analysis and reliability estimation for spectral data”, NeuroComputing, vol. 72, 2009, pp. 3590-3601.
PUB | DOI | WoS
 
[69]
2009 | Report | Veröffentlicht | PUB-ID: 1993316
M. Biehl, et al., Stationarity of Matrix Relevance Learning Vector Quantization, Machine Learning Reports, Leipzig: Universität Leipzig, 2009.
PUB
 
[68]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992534
F.-M. Schleif, et al., “Extended Targeted Profiling to Identify and Quantify Metabolites in 1-H NMR measurements”, ICOLE 2009, J. Blazewicz, K. Ecker, and B. Hammer, eds., IfI-09-12, Clausthal-Zellerfeld, Germany: Technical University of Clausthal, 2009, pp.89-103.
PUB
 
[67]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992565
S. Simmuteit, et al., “Tanimoto metric in Tree-SOM for improved representation of mass spectrometry data with an underlying taxonomic structure”, Proceedings of ICMLA 2009, IEEE Press, 2009, pp.563--567.
PUB | DOI
 
[66]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992517
F.-M. Schleif, et al., “Support Vector Classification of Proteomic Profile Spectra based on Feature Extraction with the Bi-orthogonal Discrete Wavelet Transform”, Computing and Visualization in Science, vol. 12, 2009, pp. 189-199.
PUB | DOI
 
[65]
2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993939
F.-M. Schleif, T. Villmann, and B. Hammer, “Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics”, Encyclopedia of Artificial Intelligence, J.R.-n R.-al Dopico, J. Dorado, and A. Pazos, eds., IGI Global, 2008, pp.1337-1342.
PUB
 
[64]
2008 | Report | Veröffentlicht | PUB-ID: 1993379
K. Bunte, et al., Discriminative Visualization by Limited Rank Matrix Learning, Machine Learning Reports, Leipzig: Universität Leipzig, 2008.
PUB
 
[63]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992530
F.-M. Schleif, M. Ongyerth, and T. Villmann, “Sparse coding Neural Gas for analysis of Nuclear Magnetic Resonance Spectroscopy”, Proceedings of the CBMS 2008, 2008, pp.620-625.
PUB | DOI
 
[62]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992554
P. Schneider, et al., “Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data”, Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008, M. Verleysen, ed., Evere, Belgium: d-side publications, 2008, pp.451-456.
PUB
 
[61]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992597
M. Strickert, F.-M. Schleif, and T. Villmann, “Metric adaptation for supervised attribute rating”, Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008, M. Verleysen, ed., Evere, Belgium: d-side publications, 2008, pp.31-36.
PUB
 
[60]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993966
F.-M. Schleif, T. Villmann, and B. Hammer, “Prototype based Fuzzy Classification in Clinical Proteomics”, International Journal of Approximate Reasoning, vol. 47, 2008, pp. 4-16.
PUB | DOI | WoS
 
[59]
2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993900
F.-M. Schleif, B. Hammer, and T. Villmann, “Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers”, Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications, M. Van de Werff, A. Delder, and R. Tollenaar, eds., Berlin: Springer, 2008, pp.141-167.
PUB | DOI
 
[58]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992539
F.-M. Schleif, et al., “Automatic Identification and Quantification of Metabolites in H-NMR Measurements”, Proceedings of the Workshop on Computational Systems Biology (WCSB) 2008, 2008, pp.165-168.
PUB
 
[57]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992589
M. Strickert, F.-M. Schleif, and U. Seiffert, “Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data”, Ibero-American Journal of Artificial Intelligence, vol. 37, 2008, pp. 37-44.
PUB
 
[56]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994253
T. Villmann, et al., “Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods”, Briefings in Bioinformatics, vol. 9, 2008, pp. 129-143.
PUB | DOI | WoS | PubMed | Europe PMC
 
[55]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2017617
T. Villmann, et al., “Fuzzy Classification Using Information Theoretic Learning Vector Quantization”, Neurocomputing, vol. 71, 2008, pp. 3070-3076.
PUB | DOI | WoS
 
[54]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
T. Geweniger, et al., “Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity”, ICONIP 2008, M. Köppen, N.K. Kasabov, and G.G. Coghill, eds., Berlin, Heidelberg: Springer, 2008, pp.61-69.
PUB | DOI
 
[53]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267
T. Villmann, et al., “Class imaging of hyperspectral satellite remote sensing data using FLSOM”, Proceedings of 6th International Workshop on Self-Organizing Maps, Bielefeld: Bielefeld University, 2007.
PUB | DOI
 
[52]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016
P. Schneider, et al., “Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data”, Proceedings of 6th International Workshop on Self-Organizing Maps, Bielefeld: Bielefeld University, 2007.
PUB | DOI
 
[51]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993547
B. Hammer, et al., “Intuitive Clustering of Biological Data”, Proceedings of International Joint Conference on Neural Networks, IEEE, 2007, pp.1877-1882.
PUB | DOI
 
[50]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993852
F.-M. Schleif, “Advances in pre-processing and model generation for mass spectrometric data analysis”, Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, M. Biehl, et al., eds., Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany, 2007.
PUB
 
[49]
2007 | Report | Veröffentlicht | PUB-ID: 1993922
F.-M. Schleif, A. Hasenfuss, and B. Hammer, Aggregation of multiple peak lists by use of an improved neural gas network, Leipzig: Universität Leipzig, 2007.
PUB
 
[48]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992602
M. Strickert, et al., “Derivatives of Pearson Correlation for Gradient based Analysis of Biomedical Data”, Similarity based Clustering. Lecture Notes in Artificial Intelligence, 5400, 2007.
PUB | DOI
 
[47]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993970
F.-M. Schleif, T. Villmann, and B. Hammer, “Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps”, Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578, F. Masulli, S. Mitra, and G. Pasi, eds., Berlin, Heidelberg: Springer, 2007, pp.563-570.
PUB | DOI
 
[46]
2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992509
F.-M. Schleif, “Prototypen basiertes maschinelles Lernen in der klinischen Proteomik”, Ausgezeichnete Informatikdissertationen 2006, D. Wagner, ed., GI-Edition Lecture Notes in Informatics. Dissertation, vol. 7, Bonn: Gesellschaft für Informatik, 2007, pp.179-188.
PUB
 
[45]
2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993911
F.-M. Schleif, B. Hammer, and T. Villmann, “Margin based Active Learning for LVQ Networks”, Neurocomputing, vol. 70, 2007, pp. 1215-1224.
PUB | DOI | WoS
 
[44]
2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992507
F.-M. Schleif, “Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik”, KI - Künstliche Intelligenz, 2007, pp. 65-67.
PUB
 
[43]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992610
T. Villmann, et al., “Association learning in SOMs for Fuzzy-Classification”, 6th International Conference on Machine Learning and Applications, 2007., 2007, pp.581-586.
PUB | DOI
 
[42]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992616
T. Villmann, et al., “Visualization of fuzzy information in in fuzzy-classification for image sagmentation using MDS”, Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN) 2007, M. Verleysen, ed., Evere, Belgium: d-side publications, 2007, pp.103-108.
PUB
 
[41]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993811
A. Hasenfuss, et al., “Neural gas clustering for dissimilarity data with continuous prototypes”, Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507, F. Sandoval, et al., eds., Berlin: Springer, 2007, pp.539-546.
PUB | DOI
 
[40]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992452
S.-O. Deininger, M. Gerhard, and F.-M. Schleif, “Statistical Classification and Visualization of MALDI-Imaging Data”, Proc. of CBMS 2007, 2007, pp.403-405.
PUB
 
[39]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993820
A. Hasenfuss, et al., “Neural gas clustering for sparse proximity data”, Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507, F. Sandoval, et al., eds., Berlin, Heidelberg, Germany: Springer, 2007, pp.539-546.
PUB
 
[38]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993907
F.-M. Schleif, B. Hammer, and T. Villmann, “Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra”, Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507, F. Sandoval, et al., eds., Berlin, Heidelberg: Springer, 2007, pp.1036-1044.
PUB | DOI
 
[37]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994258
T. Villmann, et al., “Fuzzy Labeled Self Organizing Map for Clasification of Spectra”, Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507, F. Sandoval, et al., eds., Berlin: Springer, 2007, pp.556-563.
PUB | DOI
 
[36]
2007 | Report | Veröffentlicht | PUB-ID: 1992505
F.-M. Schleif, Preprocessing of Nuclear Magnetic Resonance Spectrometry Data, Machine Learning Reports, Leipzig: Universität Leipzig, 2007.
PUB
 
[35]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992586
M. Strickert and F.-M. Schleif, “Supervised Attribute Relevance Determination for Protein Identification in Stress Experiments”, Proc. of MLSB 2007, 2007, pp.81-86.
PUB
 
[34]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992593
M. Strickert, F.-M. Schleif, and U. Seiffert, “Gradients of Pearson Correlation for Analysis of Biomedical Data”, Proc. of ASAI 2007, 2007, pp.139-150.
PUB
 
[33]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994184
T. Villmann, et al., “Prototype based classification using information theoretic learning”, Neural Information Processing, 13th International Conference. Proceedings, I. King, et al., eds., Lecture Notes in Computer Science, 4233, vol. Part II, Berlin: Springer, 2006, pp.40-49.
PUB
 
[32]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994273
T. Villmann, et al., “Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes”, Proceedings of Conference Artificial Neural Networks in Pattern Recognition, F. Schwenker, ed., Berlin: Springer, 2006, pp.46-56.
PUB | DOI
 
[31]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993578
B. Hammer, et al., “Supervised Batch Neural Gas”, Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR), F. Schwenker, ed., Berlin: Springer Verlag, 2006, pp.33-45.
PUB | DOI
 
[30]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993895
F.-M. Schleif, B. Hammer, and T. Villmann, “Margin based Active Learning for LVQ Networks”, Proc. Of European Symposium on Artificial Neural Networks, M. Verleysen, ed., Brussels, Belgium: d-side publications, 2006, pp.539-544.
PUB
 
[29]
2006 | Dissertation | PUB-ID: 1992511
F.-M. Schleif, Prototype based Machine Learning for Clinical Proteomics, Clausthal-Zellerfeld, Germany: Technical University Clausthal, 2006.
PUB
 
[28]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993889
F.-M. Schleif, et al., “Machine Learning and Soft-Computing in Bioinformatics. A Short Journey”, Proc. of FLINS 2006, World Scientific Press, 2006, pp.541-548.
PUB
 
[27]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993568
B. Hammer, et al., “Supervised median neural gas”, Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16, C. Dagli, et al., eds., ASME Press, 2006, pp.623-633.
PUB
 
[26]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
B. Hammer, et al., “Supervised median clustering”, 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), C.H. Dagli, ed., ASME Press series on intelligent engineering systems through artificial neural networks, 16, New York, NY: ASME Press, 2006, pp.623-632.
PUB
 
[25]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
F.-M. Schleif, et al., “Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps”, 19th IEEE International Symposium on Computer- based Medical Systems, D.J. Lee, et al., eds., Los Alamitos: IEEE Computer Society Press, 2006, pp.919-924.
PUB | DOI
 
[24]
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994237
T. Villmann, F.-M. Schleif, and B. Hammer, “Comparison of relevance learning vector quantization with other metric adaptive classification methods”, Neural Networks, vol. 19, 2006, pp. 610-622.
PUB | DOI | WoS | PubMed | Europe PMC
 
[23]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992445
C. Brüß, et al., “Fuzzy Image Segmentation with Fuzzy Labelled Neural Gas”, Proc. of ESANN 2006, 2006, pp.563-569.
PUB
 
[22]
2006 | Report | Veröffentlicht | PUB-ID: 1993584
B. Hammer, et al., Supervised median clustering, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology, 2006.
PUB
 
[21]
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
T. Villmann, et al., “Fuzzy Classification by Fuzzy Labeled Neural Gas”, Neural Networks, vol. 19, 2006, pp. 772-779.
PUB | DOI | WoS | PubMed | Europe PMC
 
[20]
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994241
T. Villmann, F.-M. Schleif, and B. Hammer, “Prototype-based fuzzy classification with local relevance for proteomics”, Neurocomputing, vol. 69, 2006, pp. 2425-2428.
PUB | DOI | WoS
 
[19]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
B. Hammer, et al., “Learning vector quantization classification with local relevance determination for medical data”, Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029, L. Rutkowski, et al., eds., Berlin, Heidelberg: Springer, 2006, pp.603-612.
PUB | DOI
 
[18]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
T. Villmann, et al., “Fuzzy Labeled Neural GAS for Fuzzy Classification”, Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM], M. Cottrell, ed., Paris, France: University Paris-1-Pantheon-Sorbonne, 2005, pp.283-290.
PUB
 
[17]
2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992513
F.-M. Schleif, “Plugins mit wxWidgets”, Offene Systeme, vol. 2005, 2005, pp. 5-10.
PUB
 
[16]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994219
T. Villmann, F.-M. Schleif, and B. Hammer, “Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization”, International Workshop on Integrative Bioinformatics, 2005.
PUB
 
[15]
2005 | Report | Veröffentlicht | PUB-ID: 1993675
B. Hammer, F.-M. Schleif, and T. Villmann, On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology, 2005.
PUB
 
[14]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993974
F.-M. Schleif, T. Villmann, and B. Hammer, “Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data”, Proceedings of the 6th Workshop on Fuzzy Logic and Applications, I. Bloch, A. Petrosino, and A.G.B. Tettamanzi, eds., Berlin, Heidelberg: Springer, 2005, pp.290-296.
PUB | DOI
 
[13]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994249
T. Villmann, F.-M. Schleif, and B. Hammer, “Fuzzy labeled soft nearest neighbor classification with relevance learning”, Proceedings of the International Conference of Machine Learning Applications, M.A. Wani, K.J. Cios, and K. Hafeez, eds., Los Angeles: IEEE Press, 2005, pp.11-15.
PUB
 
[12]
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994168
T. Villmann, B. Hammer, and F.-M. Schleif, “Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection”, Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742, H.-M. Groß, K. Debes, and H.-J. Böhme, eds., VDI Verlag, 2004, pp.592-597.
PUB
 
[11]
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994212
T. Villmann, F.-M. Schleif, and B. Hammer, “Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection”, SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior, H.-M. Groß, K. Debes, and H.-J. Böhme, eds., VDI Verlag, 2004.
PUB
 
[10]
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
F.-M. Schleif, et al., “Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data”, Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004, M.A. Wani, K.J. Cios, and K. Hafeez, eds., Los Alamitos, CA, USA: IEEE Press, 2004, pp.374-379.
PUB
 
[9]
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994223
T. Villmann, F.-M. Schleif, and B. Hammer, “Supervised Neural Gas and Relevance Learning in Learning Vector Quantization”, Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM], T. Yamakawa, ed., Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology, 2003, pp.47-52.
PUB
 
[8]
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992477
M. Köhler, et al., “A mission for the EEG coherence analysis: Is the task complex or difficult?”, Brain Topography, vol. 15, 2003, pp. 271.
PUB
 
[7]
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992456
T. Dörfler, et al., “Working memory load and EEG coherence”, Brain Topography, vol. 15, 2003, pp. 269.
PUB
 
[6]
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992466
V. Gruhn, et al., “A distributed logistic support communication system”, Proceedings of ISD 2003 - Constructing the Infrastructure for the Knowledge Economy - Methods and Tools, Theory and Practice, H. Linger, et al., eds., London: Kluwer Academic Publishers, 2003, pp.705-713.
PUB
 
[5]
2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992544
F.-M. Schleif and H. Stamer, “{LaTeX} im studentischen Alltag”, Gaotenblatt, 2002, pp. 3-10.
PUB
 
[4]
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992483
M. Köhler, et al., “Complexity and difficulty in memory based comparison”, Proceedings of the 18th Meeting of the International Society for Psychophysics, J.A. da Silva, N.P.R. Filho, and E.H. Matsushima, eds., Pabst Publishing, 2002, pp.433-439.
PUB
 
[3]
2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992515
F.-M. Schleif, “OCR mit statistischen Momenten”, Gaotenblatt, vol. 2002, 2002, pp. 15-17.
PUB
 
[2]
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992560
A. Simmel, et al., “An analysis of connections between internal and external learning process indicators using EEG coherence analysis”, Proceedings of the 17th Meeting of the International Society for Psychophysics, Pabst Publishing, 2001, pp.602-607.
PUB
 
[1]
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992461
T. Dörfler, et al., “Complexity - dependent synchronization of brain subsystems during memorization”, Proceedings of the 17th Meeting of the International Society for Psychophysics, Pabst Publishing, 2001, pp.343-348.
PUB
 

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[129]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031
B. Mokbel, et al., “Metric learning for sequences in relational LVQ”, Neurocomputing, vol. 169, 2015, pp. 306-322.
PUB | PDF | DOI | Download (ext.) | WoS
 
[128]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2910619
F.-M. Schleif, T. Villmann, and X. Zhu, “High Dimensional Matrix Relevance Learning”, 2014 IEEE International Conference on Data Mining Workshop, Piscataway, NJ: IEEE, 2015.
PUB | DOI
 
[127]
2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2910885
F.-M. Schleif, A. Gisbrecht, and P. Tino, “Large Scale Indefinite Kernel Fisher Discriminant”, Similarity-Based Pattern Recognition. Similarity-Based Pattern Recognition : Third International Workshop, SIMBAD 2015, Proceedings, A. Feragen, M. Pelillo, and M. Loog, eds., Lecture Notes in Computer Science, vol. 9370, Cham: Springer International Publishing, 2015, pp.160-170.
PUB | DOI
 
[126]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772422
A. Gisbrecht and F.-M. Schleif, “Metric and non-metric proximity transformations at linear costs”, Neurocomputing, vol. 167, 2015, pp. 643-657.
PUB | DOI | WoS
 
[125]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2759763
F.-M. Schleif, X. Zhu, and B. Hammer, “Sparse conformal prediction for dissimilarity data”, Annals of Mathematics and Artificial Intelligence, vol. 74, 2015, pp. 95-116.
PUB | DOI | WoS
 
[124]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
D. Hofmann, et al., “Learning interpretable kernelized prototype-based models”, Neurocomputing, vol. 141, 2014, pp. 84-96.
PUB | DOI | Download (ext.) | WoS
 
[123]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730
B. Hammer, et al., “Learning vector quantization for (dis-)similarities”, NeuroComputing, vol. 131, 2014, pp. 43-51.
PUB | DOI | WoS
 
[122]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2690490
M. Strickert, et al., “Correlation-based embedding of pairwise score data”, Neurocomputing, vol. 141, 2014, pp. 97-109.
PUB | DOI | WoS
 
[121]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2672504
X. Zhu, F.-M. Schleif, and B. Hammer, “Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data”, Pattern Recognition Lettters, vol. 49, 2014, pp. 138-145.
PUB | DOI | WoS
 
[120]
2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2612731
A. Micheli, F.-M. Schleif, and P. Tino, “Novel approaches in machine learning and computational intelligence”, Neurocomputing, vol. 112, 2013, pp. 1-3.
PUB | DOI | WoS
 
[119]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625202
F.-M. Schleif, X. Zhu, and B. Hammer, “Sparse prototype representation by core sets”, IDEAL 2013, et.al Hujun Yin, ed., 2013.
PUB
 
[118]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615717
X. Zhu, F.-M. Schleif, and B. Hammer, “Secure Semi-supervised Vector Quantization for Dissimilarity Data”, IWANN (1), I. Rojas, G. Joya, and J. Cabestany, eds., Lecture Notes in Computer Science, vol. 7902, Springer, 2013, pp.347-356.
PUB | DOI
 
[117]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615724
F.-M. Schleif and A. Gisbrecht, “Data Analysis of (Non-)Metric Proximities at Linear Costs”, Proceedings of SIMBAD 2013, Springer, 2013, pp.59-74.
PUB | DOI
 
[116]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615701
X. Zhu, F.-M. Schleif, and B. Hammer, “Semi-Supervised Vector Quantization for proximity data”, Proceedings of ESANN 2013, 2013, pp.89-94.
PUB
 
[115]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625232
A. Gisbrecht, et al., “Linear Time Relational Prototype Based Learning”, Int. J. Neural Syst., vol. 22, 2012.
PUB | DOI | WoS | PubMed | Europe PMC
 
[114]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615745
K. Bunte, F.-M. Schleif, and M. Biehl, “Adaptive Learning for complex-valued data”, Proceedings of ESANN 2012, 2012, pp.387-392.
PUB
 
[113]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534898
M. Biehl, et al., “Large margin linear discriminative visualization by Matrix Relevance Learning”, IJCNN, 2012, pp.1-8.
PUB | DOI
 
[112]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615750
F.-M. Schleif, et al., “Fast approximated relational and kernel clustering”, Proceedings of ICPR 2012, IEEE, 2012, pp.1229-1232.
PUB
 
[111]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615756
F.-M. Schleif, X. Zhu, and B. Hammer, “Soft Competitive Learning for large data sets”, Proceedings of MCSD 2012, 2012, pp.141-151.
PUB | DOI
 
[110]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2534839
A. Gisbrecht, et al., “Linear Time Relational Prototype Based Learning”, Int. J. Neural Syst., vol. 22, 2012, pp. 1250021.
PUB | DOI | WoS | PubMed | Europe PMC
 
[109]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534877
F.-M. Schleif, et al., “Learning Relevant Time Points for Time-Series Data in the Life Sciences”, ICANN (2), Lecture Notes in Computer Science, vol. 7553, 2012, pp.531-539.
PUB | DOI
 
[108]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
K. Bunte, et al., “Limited Rank Matrix Learning, discriminative dimension reduction and visualization”, Neural Networks, vol. 26, 2012, pp. 159-173.
PUB | DOI | WoS | PubMed | Europe PMC
 
[107]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534888
F.-M. Schleif, X. Zhu, and B. Hammer, “A Conformal Classifier for Dissimilarity Data”, AIAI (2), 2012, pp.234-243.
PUB | DOI
 
[106]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534910
X. Zhu, F.-M. Schleif, and B. Hammer, “Patch Processing for Relational Learning Vector Quantization”, ISNN (1), 2012, pp.55-63.
PUB | DOI
 
[105]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534868
B. Hammer, et al., “White Box Classification of Dissimilarity Data”, HAIS (1), 2012, pp.309-321.
PUB | DOI | WoS
 
[104]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534905
F.-M. Schleif, A. Gisbrecht, and B. Hammer, “Relevance learning for short high-dimensional time series in the life sciences”, IJCNN, 2012, pp.1-8.
PUB | DOI
 
[103]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509852
X. Zhu, et al., “Approximation techniques for clustering dissimilarity data”, Neurocomputing, vol. 90, 2012, pp. 72-84.
PUB | DOI | WoS
 
[102]
2011 | Preprint | Veröffentlicht | PUB-ID: 2534994
F.-M. Schleif, A. Gisbrecht, and B. Hammer, “Supervised learning of short and high-dimensional temporal sequences for life science measurements”, 2011.
PUB | arXiv
 
[101]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276480
A. Gisbrecht, et al., “Linear time heuristics for topographic mapping of dissimilarity data”, 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, vol. 6936, Berlin, Heidelberg: Springer, 2011, pp.25-33.
PUB | DOI
 
[100]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276485
B. Hammer, et al., “Topographic Mapping of Dissimilarity Data”, WSOM'11, 2011.
PUB
 
[99]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276492
F.-M. Schleif, A. Gisbrecht, and B. Hammer, “Accelerating Kernel Neural Gas”, ICANN'2011, S. Kaski, et al., eds., 2011.
PUB
 
[98]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276644
U. Seiffert, F.-M. Schleif, and D. Zühlke, “Recent Trends in Computational Intelligence in Life Science”, Proceedings of ESANN 2011, 2011, pp.77-86.
PUB
 
[97]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276640
K. Bunte, F.-M. Schleif, and T. Villmann, “Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization”, Proceedings of ESANN 2011, Ciaco - i6doc.com, 2011, pp.29-34.
PUB
 
[96]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2290045
J.A. Lee, F.-M. Schleif, and T. Martinetz, “Advances in artificial neural networks, machine learning, and computational intelligence”, Neurocomputing, vol. 74, 2011, pp. 1299-1300.
PUB | DOI | WoS
 
[95]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2309980
F.-M. Schleif, et al., “Efficient Kernelized Prototype-based Classification”, International Journal of Neural Systems, vol. 21, 2011, pp. 443-457.
PUB | DOI | WoS | PubMed | Europe PMC
 
[94]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276522
A. Gisbrecht, et al., “Accelerating dissimilarity clustering for biomedical data analysis”, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2011, pp.pp.154-161.
PUB
 
[93]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276654
F.-M. Schleif, “Sparse Kernel Vector Quantization with Local Dependencies”, Proceedings of IJCNN 2011, 2011, pp.accepted.
PUB
 
[92]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992489
E. Mwebaze, et al., “Divergence based classification in Learning Vector Quantization”, Neurocomputing, vol. 74, 2011, pp. 1429-1435.
PUB | DOI | WoS
 
[91]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2094556
F.-M. Schleif, et al., “Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications”, Bioinformatics, vol. 27, 2011, pp. 524-533.
PUB | DOI | WoS | PubMed | Europe PMC
 
[90]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276636
F.-M. Schleif, S. Simmuteit, and T. Villmann, “Hierarchical deconvolution of linear mixtures of high-dimensional mass spectra in micro-biology”, Proceedings of AIA 2011, 2011, pp.in press.
PUB
 
[89]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276648
P. Schneider, et al., “Multivariate class labeling in Robust Soft LVQ”, Proceedings of ESANN 2011, 2011, pp.17-22.
PUB
 
[88]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276626
S. Simmuteit, F.-M. Schleif, and T. Villmann, “Hierarchical evolving trees together with global and local learning for large data sets in MALDI imaging”, Proceedings of WCSB 2010, 2010, pp.103-106.
PUB
 
[87]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994127
T. Villmann, et al., “Divergence Based Online Learning in Vector Quantization”, Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113, L. Rutkowski, et al., eds., Berlin, Heidelberg: Springer, 2010, pp.479-486.
PUB | DOI
 
[86]
2010 | Konferenzbeitrag | Im Druck | PUB-ID: 1992498
E. Mwebaze, et al., “Divergence based Learning Vector Quantization”, Proceedings of ESANN 2010, In Press.
PUB
 
[85]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276630
F.-M. Schleif, et al., “Efficient identification and quantification of metabolites in 1-H NMR measurements by a novel data encoding approach”, Proceedings of WCSB 2010, 2010, pp.91-94.
PUB
 
[84]
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992441
C. Angulo, J.A. Lee, and F.-M. Schleif, “Advances in computational intelligence and learning”, NeuroComputing, vol. 73, 2010, pp. 1049-1050.
PUB | DOI | WoS
 
[83]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993978
F.-M. Schleif, et al., “Generalized derivative based Kernelized learning vector quantization”, Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings, C. Fyfe, et al., eds., Berlin u.a.: Springer, 2010, pp.21-28.
PUB | DOI
 
[82]
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994034
S. Simmuteit, et al., “Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints”, Knowledge and Information Systems, vol. 25, 2010, pp. 327-343.
PUB | DOI | WoS
 
[81]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992623
D. Zühlke, et al., “Learning vector quantization for heterogeneous structured data”, Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN) 2010, Evere, Belgium: d-side publications, 2010.
PUB
 
[80]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994138
T. Villmann, et al., “The Mathematics of Divergence Based Online Learning in Vector Quanitzation”, ANNPR'2010, N. El Gayar and F. Schwenker, eds., Berlin, Heidelberg: Springer, 2010, pp.108-119.
PUB
 
[79]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994227
T. Villmann, F.-M. Schleif, and B. Hammer, “Sparse representation of data”, ESANN'10, M. Verleysen, ed., D side, 2010, pp.225-234.
PUB
 
[78]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993984
F.-M. Schleif, et al., “Cancer Informatics by Prototype-networks in Mass Spectrometry”, Artificial Intelligence in Medicine, vol. 45, 2009, pp. 215-228.
PUB | DOI | WoS | PubMed | Europe PMC
 
[77]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992551
F.-M. Schleif and T. Villmann, “Neural Maps and Learning Vector Quantization - Theory and Applications”, Proceedings of the ESANN 2009. European Symposium on Artificial Neural Networks. Advances in Computational Intelligence and Learning, Evere, Belgium: d-side publications, 2009, pp.509-516.
PUB
 
[76]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992570
S. Simmuteit, et al., “Hierarchical PCA using Tree-SOM for the Identification of Bacteria”, Advances in Self-Organizing Maps. Proceedings of the 7th International Workshop on Self Organizing Maps WSOM 2009. LNCS, 5629, J.C. Príncipe and R. Miikkulainen, eds., Berlin: Springer, 2009, pp.272-280.
PUB | DOI
 
[75]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992575
S. Simmuteit, et al., “Deconvolution and Identification of Mass Spectra from mixed and pure colonies of bacteria”, ICOLE 2009, J. Blazewicz, K. Ecker, and B. Hammer, eds., IfI-09-12, Clausthal-Zellerfeld, Germany: Technical University of Clausthal, 2009, pp.104-112.
PUB
 
[74]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992607
T. Villmann and F.-M. Schleif, “Functional Vector Quantization by Neural Maps”, Proceedings of Whispers 2009, 2009.
PUB | DOI
 
[73]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994067
M. Strickert, et al., “Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data”, Similarity-based Clustering, M. Biehl, et al., eds., LNAI, 5400, Berlin: Springer, 2009, pp.70-91.
PUB | DOI
 
[72]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992547
F.-M. Schleif, M. Biehl, and A. Vellido, “Advances in machine learning and computational intelligence”, NeuroComputing, vol. 72, 2009, pp. 1377-1378.
PUB | DOI | WoS
 
[71]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992580
M. Strickert, et al., “Matrix metric adaptation for improved linear discriminant analysis of biomedical data”, Bio-Inspired Systems: Computational and Ambient Intelligence, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. LNCS, 5517, J. Cabestany, et al., eds., vol. Part 1, Berlin: Springer, 2009, pp.933-940.
PUB | DOI
 
[70]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992526
F.-M. Schleif, T. Villmann, and M. Ongyerth, “Supervised data analysis and reliability estimation for spectral data”, NeuroComputing, vol. 72, 2009, pp. 3590-3601.
PUB | DOI | WoS
 
[69]
2009 | Report | Veröffentlicht | PUB-ID: 1993316
M. Biehl, et al., Stationarity of Matrix Relevance Learning Vector Quantization, Machine Learning Reports, Leipzig: Universität Leipzig, 2009.
PUB
 
[68]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992534
F.-M. Schleif, et al., “Extended Targeted Profiling to Identify and Quantify Metabolites in 1-H NMR measurements”, ICOLE 2009, J. Blazewicz, K. Ecker, and B. Hammer, eds., IfI-09-12, Clausthal-Zellerfeld, Germany: Technical University of Clausthal, 2009, pp.89-103.
PUB
 
[67]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992565
S. Simmuteit, et al., “Tanimoto metric in Tree-SOM for improved representation of mass spectrometry data with an underlying taxonomic structure”, Proceedings of ICMLA 2009, IEEE Press, 2009, pp.563--567.
PUB | DOI
 
[66]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992517
F.-M. Schleif, et al., “Support Vector Classification of Proteomic Profile Spectra based on Feature Extraction with the Bi-orthogonal Discrete Wavelet Transform”, Computing and Visualization in Science, vol. 12, 2009, pp. 189-199.
PUB | DOI
 
[65]
2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993939
F.-M. Schleif, T. Villmann, and B. Hammer, “Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics”, Encyclopedia of Artificial Intelligence, J.R.-n R.-al Dopico, J. Dorado, and A. Pazos, eds., IGI Global, 2008, pp.1337-1342.
PUB
 
[64]
2008 | Report | Veröffentlicht | PUB-ID: 1993379
K. Bunte, et al., Discriminative Visualization by Limited Rank Matrix Learning, Machine Learning Reports, Leipzig: Universität Leipzig, 2008.
PUB
 
[63]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992530
F.-M. Schleif, M. Ongyerth, and T. Villmann, “Sparse coding Neural Gas for analysis of Nuclear Magnetic Resonance Spectroscopy”, Proceedings of the CBMS 2008, 2008, pp.620-625.
PUB | DOI
 
[62]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992554
P. Schneider, et al., “Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data”, Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008, M. Verleysen, ed., Evere, Belgium: d-side publications, 2008, pp.451-456.
PUB
 
[61]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992597
M. Strickert, F.-M. Schleif, and T. Villmann, “Metric adaptation for supervised attribute rating”, Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008, M. Verleysen, ed., Evere, Belgium: d-side publications, 2008, pp.31-36.
PUB
 
[60]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993966
F.-M. Schleif, T. Villmann, and B. Hammer, “Prototype based Fuzzy Classification in Clinical Proteomics”, International Journal of Approximate Reasoning, vol. 47, 2008, pp. 4-16.
PUB | DOI | WoS
 
[59]
2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993900
F.-M. Schleif, B. Hammer, and T. Villmann, “Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers”, Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications, M. Van de Werff, A. Delder, and R. Tollenaar, eds., Berlin: Springer, 2008, pp.141-167.
PUB | DOI
 
[58]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992539
F.-M. Schleif, et al., “Automatic Identification and Quantification of Metabolites in H-NMR Measurements”, Proceedings of the Workshop on Computational Systems Biology (WCSB) 2008, 2008, pp.165-168.
PUB
 
[57]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992589
M. Strickert, F.-M. Schleif, and U. Seiffert, “Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data”, Ibero-American Journal of Artificial Intelligence, vol. 37, 2008, pp. 37-44.
PUB
 
[56]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994253
T. Villmann, et al., “Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods”, Briefings in Bioinformatics, vol. 9, 2008, pp. 129-143.
PUB | DOI | WoS | PubMed | Europe PMC
 
[55]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2017617
T. Villmann, et al., “Fuzzy Classification Using Information Theoretic Learning Vector Quantization”, Neurocomputing, vol. 71, 2008, pp. 3070-3076.
PUB | DOI | WoS
 
[54]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
T. Geweniger, et al., “Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity”, ICONIP 2008, M. Köppen, N.K. Kasabov, and G.G. Coghill, eds., Berlin, Heidelberg: Springer, 2008, pp.61-69.
PUB | DOI
 
[53]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267
T. Villmann, et al., “Class imaging of hyperspectral satellite remote sensing data using FLSOM”, Proceedings of 6th International Workshop on Self-Organizing Maps, Bielefeld: Bielefeld University, 2007.
PUB | DOI
 
[52]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016
P. Schneider, et al., “Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data”, Proceedings of 6th International Workshop on Self-Organizing Maps, Bielefeld: Bielefeld University, 2007.
PUB | DOI
 
[51]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993547
B. Hammer, et al., “Intuitive Clustering of Biological Data”, Proceedings of International Joint Conference on Neural Networks, IEEE, 2007, pp.1877-1882.
PUB | DOI
 
[50]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993852
F.-M. Schleif, “Advances in pre-processing and model generation for mass spectrometric data analysis”, Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, M. Biehl, et al., eds., Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany, 2007.
PUB
 
[49]
2007 | Report | Veröffentlicht | PUB-ID: 1993922
F.-M. Schleif, A. Hasenfuss, and B. Hammer, Aggregation of multiple peak lists by use of an improved neural gas network, Leipzig: Universität Leipzig, 2007.
PUB
 
[48]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992602
M. Strickert, et al., “Derivatives of Pearson Correlation for Gradient based Analysis of Biomedical Data”, Similarity based Clustering. Lecture Notes in Artificial Intelligence, 5400, 2007.
PUB | DOI
 
[47]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993970
F.-M. Schleif, T. Villmann, and B. Hammer, “Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps”, Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578, F. Masulli, S. Mitra, and G. Pasi, eds., Berlin, Heidelberg: Springer, 2007, pp.563-570.
PUB | DOI
 
[46]
2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992509
F.-M. Schleif, “Prototypen basiertes maschinelles Lernen in der klinischen Proteomik”, Ausgezeichnete Informatikdissertationen 2006, D. Wagner, ed., GI-Edition Lecture Notes in Informatics. Dissertation, vol. 7, Bonn: Gesellschaft für Informatik, 2007, pp.179-188.
PUB
 
[45]
2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993911
F.-M. Schleif, B. Hammer, and T. Villmann, “Margin based Active Learning for LVQ Networks”, Neurocomputing, vol. 70, 2007, pp. 1215-1224.
PUB | DOI | WoS
 
[44]
2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992507
F.-M. Schleif, “Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik”, KI - Künstliche Intelligenz, 2007, pp. 65-67.
PUB
 
[43]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992610
T. Villmann, et al., “Association learning in SOMs for Fuzzy-Classification”, 6th International Conference on Machine Learning and Applications, 2007., 2007, pp.581-586.
PUB | DOI
 
[42]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992616
T. Villmann, et al., “Visualization of fuzzy information in in fuzzy-classification for image sagmentation using MDS”, Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN) 2007, M. Verleysen, ed., Evere, Belgium: d-side publications, 2007, pp.103-108.
PUB
 
[41]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993811
A. Hasenfuss, et al., “Neural gas clustering for dissimilarity data with continuous prototypes”, Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507, F. Sandoval, et al., eds., Berlin: Springer, 2007, pp.539-546.
PUB | DOI
 
[40]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992452
S.-O. Deininger, M. Gerhard, and F.-M. Schleif, “Statistical Classification and Visualization of MALDI-Imaging Data”, Proc. of CBMS 2007, 2007, pp.403-405.
PUB
 
[39]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993820
A. Hasenfuss, et al., “Neural gas clustering for sparse proximity data”, Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507, F. Sandoval, et al., eds., Berlin, Heidelberg, Germany: Springer, 2007, pp.539-546.
PUB
 
[38]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993907
F.-M. Schleif, B. Hammer, and T. Villmann, “Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra”, Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507, F. Sandoval, et al., eds., Berlin, Heidelberg: Springer, 2007, pp.1036-1044.
PUB | DOI
 
[37]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994258
T. Villmann, et al., “Fuzzy Labeled Self Organizing Map for Clasification of Spectra”, Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507, F. Sandoval, et al., eds., Berlin: Springer, 2007, pp.556-563.
PUB | DOI
 
[36]
2007 | Report | Veröffentlicht | PUB-ID: 1992505
F.-M. Schleif, Preprocessing of Nuclear Magnetic Resonance Spectrometry Data, Machine Learning Reports, Leipzig: Universität Leipzig, 2007.
PUB
 
[35]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992586
M. Strickert and F.-M. Schleif, “Supervised Attribute Relevance Determination for Protein Identification in Stress Experiments”, Proc. of MLSB 2007, 2007, pp.81-86.
PUB
 
[34]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992593
M. Strickert, F.-M. Schleif, and U. Seiffert, “Gradients of Pearson Correlation for Analysis of Biomedical Data”, Proc. of ASAI 2007, 2007, pp.139-150.
PUB
 
[33]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994184
T. Villmann, et al., “Prototype based classification using information theoretic learning”, Neural Information Processing, 13th International Conference. Proceedings, I. King, et al., eds., Lecture Notes in Computer Science, 4233, vol. Part II, Berlin: Springer, 2006, pp.40-49.
PUB
 
[32]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994273
T. Villmann, et al., “Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes”, Proceedings of Conference Artificial Neural Networks in Pattern Recognition, F. Schwenker, ed., Berlin: Springer, 2006, pp.46-56.
PUB | DOI
 
[31]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993578
B. Hammer, et al., “Supervised Batch Neural Gas”, Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR), F. Schwenker, ed., Berlin: Springer Verlag, 2006, pp.33-45.
PUB | DOI
 
[30]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993895
F.-M. Schleif, B. Hammer, and T. Villmann, “Margin based Active Learning for LVQ Networks”, Proc. Of European Symposium on Artificial Neural Networks, M. Verleysen, ed., Brussels, Belgium: d-side publications, 2006, pp.539-544.
PUB
 
[29]
2006 | Dissertation | PUB-ID: 1992511
F.-M. Schleif, Prototype based Machine Learning for Clinical Proteomics, Clausthal-Zellerfeld, Germany: Technical University Clausthal, 2006.
PUB
 
[28]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993889
F.-M. Schleif, et al., “Machine Learning and Soft-Computing in Bioinformatics. A Short Journey”, Proc. of FLINS 2006, World Scientific Press, 2006, pp.541-548.
PUB
 
[27]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993568
B. Hammer, et al., “Supervised median neural gas”, Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16, C. Dagli, et al., eds., ASME Press, 2006, pp.623-633.
PUB
 
[26]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
B. Hammer, et al., “Supervised median clustering”, 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), C.H. Dagli, ed., ASME Press series on intelligent engineering systems through artificial neural networks, 16, New York, NY: ASME Press, 2006, pp.623-632.
PUB
 
[25]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
F.-M. Schleif, et al., “Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps”, 19th IEEE International Symposium on Computer- based Medical Systems, D.J. Lee, et al., eds., Los Alamitos: IEEE Computer Society Press, 2006, pp.919-924.
PUB | DOI
 
[24]
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994237
T. Villmann, F.-M. Schleif, and B. Hammer, “Comparison of relevance learning vector quantization with other metric adaptive classification methods”, Neural Networks, vol. 19, 2006, pp. 610-622.
PUB | DOI | WoS | PubMed | Europe PMC
 
[23]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992445
C. Brüß, et al., “Fuzzy Image Segmentation with Fuzzy Labelled Neural Gas”, Proc. of ESANN 2006, 2006, pp.563-569.
PUB
 
[22]
2006 | Report | Veröffentlicht | PUB-ID: 1993584
B. Hammer, et al., Supervised median clustering, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology, 2006.
PUB
 
[21]
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
T. Villmann, et al., “Fuzzy Classification by Fuzzy Labeled Neural Gas”, Neural Networks, vol. 19, 2006, pp. 772-779.
PUB | DOI | WoS | PubMed | Europe PMC
 
[20]
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994241
T. Villmann, F.-M. Schleif, and B. Hammer, “Prototype-based fuzzy classification with local relevance for proteomics”, Neurocomputing, vol. 69, 2006, pp. 2425-2428.
PUB | DOI | WoS
 
[19]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
B. Hammer, et al., “Learning vector quantization classification with local relevance determination for medical data”, Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029, L. Rutkowski, et al., eds., Berlin, Heidelberg: Springer, 2006, pp.603-612.
PUB | DOI
 
[18]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
T. Villmann, et al., “Fuzzy Labeled Neural GAS for Fuzzy Classification”, Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM], M. Cottrell, ed., Paris, France: University Paris-1-Pantheon-Sorbonne, 2005, pp.283-290.
PUB
 
[17]
2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992513
F.-M. Schleif, “Plugins mit wxWidgets”, Offene Systeme, vol. 2005, 2005, pp. 5-10.
PUB
 
[16]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994219
T. Villmann, F.-M. Schleif, and B. Hammer, “Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization”, International Workshop on Integrative Bioinformatics, 2005.
PUB
 
[15]
2005 | Report | Veröffentlicht | PUB-ID: 1993675
B. Hammer, F.-M. Schleif, and T. Villmann, On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology, 2005.
PUB
 
[14]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993974
F.-M. Schleif, T. Villmann, and B. Hammer, “Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data”, Proceedings of the 6th Workshop on Fuzzy Logic and Applications, I. Bloch, A. Petrosino, and A.G.B. Tettamanzi, eds., Berlin, Heidelberg: Springer, 2005, pp.290-296.
PUB | DOI
 
[13]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994249
T. Villmann, F.-M. Schleif, and B. Hammer, “Fuzzy labeled soft nearest neighbor classification with relevance learning”, Proceedings of the International Conference of Machine Learning Applications, M.A. Wani, K.J. Cios, and K. Hafeez, eds., Los Angeles: IEEE Press, 2005, pp.11-15.
PUB
 
[12]
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994168
T. Villmann, B. Hammer, and F.-M. Schleif, “Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection”, Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742, H.-M. Groß, K. Debes, and H.-J. Böhme, eds., VDI Verlag, 2004, pp.592-597.
PUB
 
[11]
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994212
T. Villmann, F.-M. Schleif, and B. Hammer, “Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection”, SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior, H.-M. Groß, K. Debes, and H.-J. Böhme, eds., VDI Verlag, 2004.
PUB
 
[10]
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
F.-M. Schleif, et al., “Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data”, Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004, M.A. Wani, K.J. Cios, and K. Hafeez, eds., Los Alamitos, CA, USA: IEEE Press, 2004, pp.374-379.
PUB
 
[9]
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994223
T. Villmann, F.-M. Schleif, and B. Hammer, “Supervised Neural Gas and Relevance Learning in Learning Vector Quantization”, Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM], T. Yamakawa, ed., Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology, 2003, pp.47-52.
PUB
 
[8]
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992477
M. Köhler, et al., “A mission for the EEG coherence analysis: Is the task complex or difficult?”, Brain Topography, vol. 15, 2003, pp. 271.
PUB
 
[7]
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992456
T. Dörfler, et al., “Working memory load and EEG coherence”, Brain Topography, vol. 15, 2003, pp. 269.
PUB
 
[6]
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992466
V. Gruhn, et al., “A distributed logistic support communication system”, Proceedings of ISD 2003 - Constructing the Infrastructure for the Knowledge Economy - Methods and Tools, Theory and Practice, H. Linger, et al., eds., London: Kluwer Academic Publishers, 2003, pp.705-713.
PUB
 
[5]
2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992544
F.-M. Schleif and H. Stamer, “{LaTeX} im studentischen Alltag”, Gaotenblatt, 2002, pp. 3-10.
PUB
 
[4]
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992483
M. Köhler, et al., “Complexity and difficulty in memory based comparison”, Proceedings of the 18th Meeting of the International Society for Psychophysics, J.A. da Silva, N.P.R. Filho, and E.H. Matsushima, eds., Pabst Publishing, 2002, pp.433-439.
PUB
 
[3]
2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992515
F.-M. Schleif, “OCR mit statistischen Momenten”, Gaotenblatt, vol. 2002, 2002, pp. 15-17.
PUB
 
[2]
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992560
A. Simmel, et al., “An analysis of connections between internal and external learning process indicators using EEG coherence analysis”, Proceedings of the 17th Meeting of the International Society for Psychophysics, Pabst Publishing, 2001, pp.602-607.
PUB
 
[1]
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992461
T. Dörfler, et al., “Complexity - dependent synchronization of brain subsystems during memorization”, Proceedings of the 17th Meeting of the International Society for Psychophysics, Pabst Publishing, 2001, pp.343-348.
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