129 Publikationen

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[129]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031
Mokbel, B., et al., 2015. Metric learning for sequences in relational LVQ. Neurocomputing, 169(SI), p 306-322.
PUB | PDF | DOI | Download (ext.) | WoS
 
[128]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2910619
Schleif, F.-M., Villmann, T., & Zhu, X., 2015. High Dimensional Matrix Relevance Learning. In 2014 IEEE International Conference on Data Mining Workshop. Piscataway, NJ: IEEE.
PUB | DOI
 
[127]
2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2910885
Schleif, F.-M., Gisbrecht, A., & Tino, P., 2015. Large Scale Indefinite Kernel Fisher Discriminant. In A. Feragen, M. Pelillo, & M. Loog, eds. Similarity-Based Pattern Recognition. Similarity-Based Pattern Recognition : Third International Workshop, SIMBAD 2015, Proceedings. Lecture Notes in Computer Science. no.9370 Cham: Springer International Publishing, pp. 160-170.
PUB | DOI
 
[126]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772422
Gisbrecht, A., & Schleif, F.-M., 2015. Metric and non-metric proximity transformations at linear costs. Neurocomputing, 167, p 643-657.
PUB | DOI | WoS
 
[125]
2015 | Zeitschriftenaufsatz | Veröffentlicht | 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), p 95-116.
PUB | DOI | WoS
 
[124]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
Hofmann, D., et al., 2014. Learning interpretable kernelized prototype-based models. Neurocomputing, 141, p 84-96.
PUB | DOI | Download (ext.) | WoS
 
[123]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730
Hammer, B., et al., 2014. Learning vector quantization for (dis-)similarities. NeuroComputing, 131, p 43-51.
PUB | DOI | WoS
 
[122]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2690490
Strickert, M., et al., 2014. Correlation-based embedding of pairwise score data. Neurocomputing, 141, p 97-109.
PUB | DOI | WoS
 
[121]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2672504
Zhu, X., Schleif, F.-M., & Hammer, B., 2014. Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data. Pattern Recognition Lettters, 49, p 138-145.
PUB | DOI | WoS
 
[120]
2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2612731
Micheli, A., Schleif, F.-M., & Tino, P., 2013. Novel approaches in machine learning and computational intelligence. Neurocomputing, 112, p 1-3.
PUB | DOI | WoS
 
[119]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625202
Schleif, F.-M., Zhu, X., & Hammer, B., 2013. Sparse prototype representation by core sets. In et.al Hujun Yin, ed. IDEAL 2013.
PUB
 
[118]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615717
Zhu, X., Schleif, F.-M., & Hammer, B., 2013. Secure Semi-supervised Vector Quantization for Dissimilarity Data. In I. Rojas, G. Joya, & J. Cabestany, eds. IWANN (1). Lecture Notes in Computer Science. no.7902 Springer, pp. 347-356.
PUB | DOI
 
[117]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615724
Schleif, F.-M., & Gisbrecht, A., 2013. Data Analysis of (Non-)Metric Proximities at Linear Costs. In Proceedings of SIMBAD 2013. Springer, pp. 59-74.
PUB | DOI
 
[116]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615701
Zhu, X., Schleif, F.-M., & Hammer, B., 2013. Semi-Supervised Vector Quantization for proximity data. In Proceedings of ESANN 2013. pp. 89-94.
PUB
 
[115]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625232
Gisbrecht, A., et al., 2012. Linear Time Relational Prototype Based Learning. Int. J. Neural Syst., 22(5).
PUB | DOI | WoS | PubMed | Europe PMC
 
[114]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615745
Bunte, K., Schleif, F.-M., & Biehl, M., 2012. Adaptive Learning for complex-valued data. In Proceedings of ESANN 2012. pp. 387-392.
PUB
 
[113]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534898
Biehl, M., et al., 2012. Large margin linear discriminative visualization by Matrix Relevance Learning. In IJCNN. pp. 1-8.
PUB | DOI
 
[112]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615750
Schleif, F.-M., et al., 2012. Fast approximated relational and kernel clustering. In Proceedings of ICPR 2012. IEEE, pp. 1229-1232.
PUB
 
[111]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615756
Schleif, F.-M., Zhu, X., & Hammer, B., 2012. Soft Competitive Learning for large data sets. In Proceedings of MCSD 2012. pp. 141-151.
PUB | DOI
 
[110]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2534839
Gisbrecht, A., et al., 2012. Linear Time Relational Prototype Based Learning. Int. J. Neural Syst., 22(05), p 1250021.
PUB | DOI | WoS | PubMed | Europe PMC
 
[109]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534877
Schleif, F.-M., et al., 2012. Learning Relevant Time Points for Time-Series Data in the Life Sciences. In ICANN (2). Lecture Notes in Computer Science. no.7553 pp. 531-539.
PUB | DOI
 
[108]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
Bunte, K., et al., 2012. Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks, 26, p 159-173.
PUB | DOI | WoS | PubMed | Europe PMC
 
[107]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534888
Schleif, F.-M., Zhu, X., & Hammer, B., 2012. A Conformal Classifier for Dissimilarity Data. In AIAI (2). pp. 234-243.
PUB | DOI
 
[106]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534910
Zhu, X., Schleif, F.-M., & Hammer, B., 2012. Patch Processing for Relational Learning Vector Quantization. In ISNN (1). pp. 55-63.
PUB | DOI
 
[105]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534868
Hammer, B., et al., 2012. White Box Classification of Dissimilarity Data. In HAIS (1). pp. 309-321.
PUB | DOI | WoS
 
[104]
2012 | Konferenzbeitrag | Veröffentlicht | 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. pp. 1-8.
PUB | DOI
 
[103]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509852
Zhu, X., et al., 2012. Approximation techniques for clustering dissimilarity data. Neurocomputing, 90, p 72-84.
PUB | DOI | WoS
 
[102]
2011 | Preprint | Veröffentlicht | PUB-ID: 2534994
Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2011. Supervised learning of short and high-dimensional temporal sequences for life science measurements.
PUB | arXiv
 
[101]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276480
Gisbrecht, A., et al., 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. no.6936 Berlin, Heidelberg: Springer, pp. 25-33.
PUB | DOI
 
[100]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276485
Hammer, B., et al., 2011. Topographic Mapping of Dissimilarity Data. In WSOM'11.
PUB
 
[99]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276492
Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2011. Accelerating Kernel Neural Gas. In S. Kaski, et al., eds. ICANN'2011.
PUB
 
[98]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276644
Seiffert, U., Schleif, F.-M., & Zühlke, D., 2011. Recent Trends in Computational Intelligence in Life Science. In Proceedings of ESANN 2011. pp. 77-86.
PUB
 
[97]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276640
Bunte, K., Schleif, F.-M., & Villmann, T., 2011. Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. In Proceedings of ESANN 2011. Ciaco - i6doc.com, pp. 29-34.
PUB
 
[96]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2290045
Lee, J.A., Schleif, F.-M., & Martinetz, T., 2011. Advances in artificial neural networks, machine learning, and computational intelligence. Neurocomputing, 74(9), p 1299-1300.
PUB | DOI | WoS
 
[95]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2309980
Schleif, F.-M., et al., 2011. Efficient Kernelized Prototype-based Classification. International Journal of Neural Systems, 21(06), p 443-457.
PUB | DOI | WoS | PubMed | Europe PMC
 
[94]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276522
Gisbrecht, A., et al., 2011. Accelerating dissimilarity clustering for biomedical data analysis. In IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp. pp.154-161.
PUB
 
[93]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276654
Schleif, F.-M., 2011. Sparse Kernel Vector Quantization with Local Dependencies. In Proceedings of IJCNN 2011. pp. accepted.
PUB
 
[92]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992489
Mwebaze, E., et al., 2011. Divergence based classification in Learning Vector Quantization. Neurocomputing, 74(9), p 1429-1435.
PUB | DOI | WoS
 
[91]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2094556
Schleif, F.-M., et al., 2011. Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications. Bioinformatics, 27(4), p 524-533.
PUB | DOI | WoS | PubMed | Europe PMC
 
[90]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276636
Schleif, F.-M., Simmuteit, S., & Villmann, T., 2011. Hierarchical deconvolution of linear mixtures of high-dimensional mass spectra in micro-biology. In Proceedings of AIA 2011. pp. in press.
PUB
 
[89]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276648
Schneider, P., et al., 2011. Multivariate class labeling in Robust Soft LVQ. In Proceedings of ESANN 2011. pp. 17-22.
PUB
 
[88]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276626
Simmuteit, S., Schleif, F.-M., & Villmann, T., 2010. Hierarchical evolving trees together with global and local learning for large data sets in MALDI imaging. In Proceedings of WCSB 2010. pp. 103-106.
PUB
 
[87]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994127
Villmann, T., et al., 2010. Divergence Based Online Learning in Vector Quantization. In L. Rutkowski, et al., eds. Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Berlin, Heidelberg: Springer, pp. 479-486.
PUB | DOI
 
[86]
2010 | Konferenzbeitrag | Im Druck | PUB-ID: 1992498
Mwebaze, E., et al., In Press. Divergence based Learning Vector Quantization. In Proceedings of ESANN 2010.
PUB
 
[85]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276630
Schleif, F.-M., et al., 2010. Efficient identification and quantification of metabolites in 1-H NMR measurements by a novel data encoding approach. In Proceedings of WCSB 2010. pp. 91-94.
PUB
 
[84]
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992441
Angulo, C., Lee, J.A., & Schleif, F.-M., 2010. Advances in computational intelligence and learning. NeuroComputing, 73(7-9), p 1049-1050.
PUB | DOI | WoS
 
[83]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993978
Schleif, F.-M., et al., 2010. Generalized derivative based Kernelized learning vector quantization. In C. Fyfe, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Berlin u.a.: Springer, pp. 21-28.
PUB | DOI
 
[82]
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994034
Simmuteit, S., et al., 2010. Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowledge and Information Systems, 25(2), p 327-343.
PUB | DOI | WoS
 
[81]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992623
Zühlke, D., et al., 2010. Learning vector quantization for heterogeneous structured data. In Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN) 2010. Evere, Belgium: d-side publications.
PUB
 
[80]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994138
Villmann, T., et al., 2010. The Mathematics of Divergence Based Online Learning in Vector Quanitzation. In N. El Gayar & F. Schwenker, eds. ANNPR'2010. Berlin, Heidelberg: Springer, pp. 108-119.
PUB
 
[79]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994227
Villmann, T., Schleif, F.-M., & Hammer, B., 2010. Sparse representation of data. In M. Verleysen, ed. ESANN'10. D side, pp. 225-234.
PUB
 
[78]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993984
Schleif, F.-M., et al., 2009. Cancer Informatics by Prototype-networks in Mass Spectrometry. Artificial Intelligence in Medicine, 45(2-3), p 215-228.
PUB | DOI | WoS | PubMed | Europe PMC
 
[77]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992551
Schleif, F.-M., & Villmann, T., 2009. Neural Maps and Learning Vector Quantization - Theory and Applications. In Proceedings of the ESANN 2009. European Symposium on Artificial Neural Networks. Advances in Computational Intelligence and Learning. Evere, Belgium: d-side publications, pp. 509-516.
PUB
 
[76]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992570
Simmuteit, S., et al., 2009. Hierarchical PCA using Tree-SOM for the Identification of Bacteria. In J. C. Príncipe & R. Miikkulainen, eds. Advances in Self-Organizing Maps. Proceedings of the 7th International Workshop on Self Organizing Maps WSOM 2009. LNCS, 5629. Berlin: Springer, pp. 272-280.
PUB | DOI
 
[75]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992575
Simmuteit, S., et al., 2009. Deconvolution and Identification of Mass Spectra from mixed and pure colonies of bacteria. In J. Blazewicz, K. Ecker, & B. Hammer, eds. ICOLE 2009. IfI-09-12. Clausthal-Zellerfeld, Germany: Technical University of Clausthal, pp. 104-112.
PUB
 
[74]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992607
Villmann, T., & Schleif, F.-M., 2009. Functional Vector Quantization by Neural Maps. In Proceedings of Whispers 2009.
PUB | DOI
 
[73]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994067
Strickert, M., et al., 2009. Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. In M. Biehl, et al., eds. Similarity-based Clustering. LNAI, 5400. Berlin: Springer, pp. 70-91.
PUB | DOI
 
[72]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992547
Schleif, F.-M., Biehl, M., & Vellido, A., 2009. Advances in machine learning and computational intelligence. NeuroComputing, 72(7-9), p 1377-1378.
PUB | DOI | WoS
 
[71]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992580
Strickert, M., et al., 2009. Matrix metric adaptation for improved linear discriminant analysis of biomedical data. In J. Cabestany, et al., eds. Bio-Inspired Systems: Computational and Ambient Intelligence, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. LNCS, 5517. no.Part 1 Berlin: Springer, pp. 933-940.
PUB | DOI
 
[70]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992526
Schleif, F.-M., Villmann, T., & Ongyerth, M., 2009. Supervised data analysis and reliability estimation for spectral data. NeuroComputing, 72(16-18), p 3590-3601.
PUB | DOI | WoS
 
[69]
2009 | Report | Veröffentlicht | PUB-ID: 1993316
Biehl, M., et al., 2009. Stationarity of Matrix Relevance Learning Vector Quantization, Machine Learning Reports, Leipzig: Universität Leipzig.
PUB
 
[68]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992534
Schleif, F.-M., et al., 2009. Extended Targeted Profiling to Identify and Quantify Metabolites in 1-H NMR measurements. In J. Blazewicz, K. Ecker, & B. Hammer, eds. ICOLE 2009. IfI-09-12. Clausthal-Zellerfeld, Germany: Technical University of Clausthal, pp. 89-103.
PUB
 
[67]
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992565
Simmuteit, S., et al., 2009. Tanimoto metric in Tree-SOM for improved representation of mass spectrometry data with an underlying taxonomic structure. In Proceedings of ICMLA 2009. IEEE Press, pp. 563--567.
PUB | DOI
 
[66]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992517
Schleif, F.-M., et al., 2009. Support Vector Classification of Proteomic Profile Spectra based on Feature Extraction with the Bi-orthogonal Discrete Wavelet Transform. Computing and Visualization in Science, 12(4), p 189-199.
PUB | DOI
 
[65]
2008 | Sammelwerksbeitrag | Veröffentlicht | 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 J. R. -n R. -al Dopico, J. Dorado, & A. Pazos, eds. Encyclopedia of Artificial Intelligence. IGI Global, pp. 1337-1342.
PUB
 
[64]
2008 | Report | Veröffentlicht | PUB-ID: 1993379
Bunte, K., et al., 2008. Discriminative Visualization by Limited Rank Matrix Learning, Machine Learning Reports, Leipzig: Universität Leipzig.
PUB
 
[63]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992530
Schleif, F.-M., Ongyerth, M., & Villmann, T., 2008. Sparse coding Neural Gas for analysis of Nuclear Magnetic Resonance Spectroscopy. In Proceedings of the CBMS 2008. pp. 620-625.
PUB | DOI
 
[62]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992554
Schneider, P., et al., 2008. Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data. In M. Verleysen, ed. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008. Evere, Belgium: d-side publications, pp. 451-456.
PUB
 
[61]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992597
Strickert, M., Schleif, F.-M., & Villmann, T., 2008. Metric adaptation for supervised attribute rating. In M. Verleysen, ed. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008. Evere, Belgium: d-side publications, pp. 31-36.
PUB
 
[60]
2008 | Zeitschriftenaufsatz | Veröffentlicht | 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), p 4-16.
PUB | DOI | WoS
 
[59]
2008 | Sammelwerksbeitrag | Veröffentlicht | 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 M. Van de Werff, A. Delder, & R. Tollenaar, eds. Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Berlin: Springer, pp. 141-167.
PUB | DOI
 
[58]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992539
Schleif, F.-M., et al., 2008. Automatic Identification and Quantification of Metabolites in H-NMR Measurements. In Proceedings of the Workshop on Computational Systems Biology (WCSB) 2008. pp. 165-168.
PUB
 
[57]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992589
Strickert, M., Schleif, F.-M., & Seiffert, U., 2008. Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Ibero-American Journal of Artificial Intelligence, 37(12), p 37-44.
PUB
 
[56]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994253
Villmann, T., et al., 2008. Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics, 9(2), p 129-143.
PUB | DOI | WoS | PubMed | Europe PMC
 
[55]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2017617
Villmann, T., et al., 2008. Fuzzy Classification Using Information Theoretic Learning Vector Quantization. Neurocomputing, 71(16-18), p 3070-3076.
PUB | DOI | WoS
 
[54]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
Geweniger, T., et al., 2008. Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity. In M. Köppen, N. K. Kasabov, & G. G. Coghill, eds. ICONIP 2008. Berlin, Heidelberg: Springer, pp. 61-69.
PUB | DOI
 
[53]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267
Villmann, T., et al., 2007. Class imaging of hyperspectral satellite remote sensing data using FLSOM. In Proceedings of 6th International Workshop on Self-Organizing Maps. Bielefeld: Bielefeld University.
PUB | DOI
 
[52]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016
Schneider, P., et al., 2007. Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data. In Proceedings of 6th International Workshop on Self-Organizing Maps. Bielefeld: Bielefeld University.
PUB | DOI
 
[51]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993547
Hammer, B., et al., 2007. Intuitive Clustering of Biological Data. In Proceedings of International Joint Conference on Neural Networks. IEEE, pp. 1877-1882.
PUB | DOI
 
[50]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993852
Schleif, F.-M., 2007. Advances in pre-processing and model generation for mass spectrometric data analysis. In M. Biehl, et al., eds. Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings. Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany.
PUB
 
[49]
2007 | Report | Veröffentlicht | 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
 
[48]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992602
Strickert, M., et al., 2007. Derivatives of Pearson Correlation for Gradient based Analysis of Biomedical Data. In Similarity based Clustering. Lecture Notes in Artificial Intelligence, 5400.
PUB | DOI
 
[47]
2007 | Konferenzbeitrag | Veröffentlicht | 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 F. Masulli, S. Mitra, & G. Pasi, eds. Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Berlin, Heidelberg: Springer, pp. 563-570.
PUB | DOI
 
[46]
2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992509
Schleif, F.-M., 2007. Prototypen basiertes maschinelles Lernen in der klinischen Proteomik. In D. Wagner, ed. Ausgezeichnete Informatikdissertationen 2006. GI-Edition Lecture Notes in Informatics. Dissertation. no.7 Bonn: Gesellschaft für Informatik, pp. 179-188.
PUB
 
[45]
2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993911
Schleif, F.-M., Hammer, B., & Villmann, T., 2007. Margin based Active Learning for LVQ Networks. Neurocomputing, 70(7-9), p 1215-1224.
PUB | DOI | WoS
 
[44]
2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992507
Schleif, F.-M., 2007. Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik. KI - Künstliche Intelligenz, (4), p 65-67.
PUB
 
[43]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992610
Villmann, T., et al., 2007. Association learning in SOMs for Fuzzy-Classification. In 6th International Conference on Machine Learning and Applications, 2007. pp. 581-586.
PUB | DOI
 
[42]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993811
Hasenfuss, A., et al., 2007. Neural gas clustering for dissimilarity data with continuous prototypes. In F. Sandoval, et al., eds. Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Berlin: Springer, pp. 539-546.
PUB | DOI
 
[41]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992616
Villmann, T., et al., 2007. Visualization of fuzzy information in in fuzzy-classification for image sagmentation using MDS. In M. Verleysen, ed. Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN) 2007. Evere, Belgium: d-side publications, pp. 103-108.
PUB
 
[40]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992452
Deininger, S.-O., Gerhard, M., & Schleif, F.-M., 2007. Statistical Classification and Visualization of MALDI-Imaging Data. In Proc. of CBMS 2007. pp. 403-405.
PUB
 
[39]
2007 | Report | Veröffentlicht | PUB-ID: 1992505
Schleif, F.-M., 2007. Preprocessing of Nuclear Magnetic Resonance Spectrometry Data, Machine Learning Reports, Leipzig: Universität Leipzig.
PUB
 
[38]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992586
Strickert, M., & Schleif, F.-M., 2007. Supervised Attribute Relevance Determination for Protein Identification in Stress Experiments. In Proc. of MLSB 2007. pp. 81-86.
PUB
 
[37]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992593
Strickert, M., Schleif, F.-M., & Seiffert, U., 2007. Gradients of Pearson Correlation for Analysis of Biomedical Data. In Proc. of ASAI 2007. pp. 139-150.
PUB
 
[36]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993820
Hasenfuss, A., et al., 2007. Neural gas clustering for sparse proximity data. In F. Sandoval, et al., eds. Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Berlin, Heidelberg, Germany: Springer, pp. 539-546.
PUB
 
[35]
2007 | Konferenzbeitrag | Veröffentlicht | 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 F. Sandoval, et al., eds. Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Berlin, Heidelberg: Springer, pp. 1036-1044.
PUB | DOI
 
[34]
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994258
Villmann, T., et al., 2007. Fuzzy Labeled Self Organizing Map for Clasification of Spectra. In F. Sandoval, et al., eds. Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Berlin: Springer, pp. 556-563.
PUB | DOI
 
[33]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993578
Hammer, B., et al., 2006. Supervised Batch Neural Gas. In F. Schwenker, ed. Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR). Berlin: Springer Verlag, pp. 33-45.
PUB | DOI
 
[32]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993895
Schleif, F.-M., Hammer, B., & Villmann, T., 2006. Margin based Active Learning for LVQ Networks. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks. Brussels, Belgium: d-side publications, pp. 539-544.
PUB
 
[31]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994184
Villmann, T., et al., 2006. Prototype based classification using information theoretic learning. In I. King, et al., eds. Neural Information Processing, 13th International Conference. Proceedings. Lecture Notes in Computer Science, 4233. no.Part II Berlin: Springer, pp. 40-49.
PUB
 
[30]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994273
Villmann, T., et al., 2006. Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. In F. Schwenker, ed. Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Berlin: Springer, pp. 46-56.
PUB | DOI
 
[29]
2006 | Dissertation | PUB-ID: 1992511
Schleif, F.-M., 2006. Prototype based Machine Learning for Clinical Proteomics, Clausthal-Zellerfeld, Germany: Technical University Clausthal.
PUB
 
[28]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993889
Schleif, F.-M., et al., 2006. Machine Learning and Soft-Computing in Bioinformatics. A Short Journey. In Proc. of FLINS 2006. World Scientific Press, pp. 541-548.
PUB
 
[27]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993568
Hammer, B., et al., 2006. Supervised median neural gas. In C. Dagli, et al., eds. Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. ASME Press, pp. 623-633.
PUB
 
[26]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
Hammer, B., et al., 2006. Supervised median clustering. In C. H. Dagli, ed. 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). ASME Press series on intelligent engineering systems through artificial neural networks, 16. New York, NY: ASME Press, pp. 623-632.
PUB
 
[25]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
Schleif, F.-M., et al., 2006. Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps. In D. J. Lee, et al., eds. 19th IEEE International Symposium on Computer- based Medical Systems. Los Alamitos: IEEE Computer Society Press, pp. 919-924.
PUB | DOI
 
[24]
2006 | Zeitschriftenaufsatz | Veröffentlicht | 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), p 610-622.
PUB | DOI | WoS | PubMed | Europe PMC
 
[23]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992445
Brüß, C., et al., 2006. Fuzzy Image Segmentation with Fuzzy Labelled Neural Gas. In Proc. of ESANN 2006. pp. 563-569.
PUB
 
[22]
2006 | Report | Veröffentlicht | PUB-ID: 1993584
Hammer, B., et al., 2006. Supervised median clustering, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
 
[21]
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
Villmann, T., et al., 2006. Fuzzy Classification by Fuzzy Labeled Neural Gas. Neural Networks, 19(6-7), p 772-779.
PUB | DOI | WoS | PubMed | Europe PMC
 
[20]
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994241
Villmann, T., Schleif, F.-M., & Hammer, B., 2006. Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing, 69(16-18), p 2425-2428.
PUB | DOI | WoS
 
[19]
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
Hammer, B., et al., 2006. Learning vector quantization classification with local relevance determination for medical data. In L. Rutkowski, et al., eds. Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Berlin, Heidelberg: Springer, pp. 603-612.
PUB | DOI
 
[18]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
Villmann, T., et al., 2005. Fuzzy Labeled Neural GAS for Fuzzy Classification. In M. Cottrell, ed. Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Paris, France: University Paris-1-Pantheon-Sorbonne, pp. 283-290.
PUB
 
[17]
2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992513
Schleif, F.-M., 2005. Plugins mit wxWidgets. Offene Systeme, 2005(1), p 5-10.
PUB
 
[16]
2005 | Konferenzbeitrag | Veröffentlicht | 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
 
[15]
2005 | Report | Veröffentlicht | 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
 
[14]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993974
Schleif, F.-M., Villmann, T., & Hammer, B., 2005. Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. In I. Bloch, A. Petrosino, & A. G. B. Tettamanzi, eds. Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Berlin, Heidelberg: Springer, pp. 290-296.
PUB | DOI
 
[13]
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994249
Villmann, T., Schleif, F.-M., & Hammer, B., 2005. Fuzzy labeled soft nearest neighbor classification with relevance learning. In M. A. Wani, K. J. Cios, & K. Hafeez, eds. Proceedings of the International Conference of Machine Learning Applications. Los Angeles: IEEE Press, pp. 11-15.
PUB
 
[12]
2004 | Konferenzbeitrag | Veröffentlicht | 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 H. - M. Groß, K. Debes, & H. - J. Böhme, eds. Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. VDI Verlag, pp. 592-597.
PUB
 
[11]
2004 | Konferenzbeitrag | Veröffentlicht | 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 H. - M. Groß, K. Debes, & H. - J. Böhme, eds. SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. VDI Verlag.
PUB
 
[10]
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
Schleif, F.-M., et al., 2004. Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data. In M. A. Wani, K. J. Cios, & K. Hafeez, eds. Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Los Alamitos, CA, USA: IEEE Press, pp. 374-379.
PUB
 
[9]
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994223
Villmann, T., Schleif, F.-M., & Hammer, B., 2003. Supervised Neural Gas and Relevance Learning in Learning Vector Quantization. In T. Yamakawa, ed. Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology, pp. 47-52.
PUB
 
[8]
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992477
Köhler, M., et al., 2003. A mission for the EEG coherence analysis: Is the task complex or difficult? Brain Topography, 15(4), p 271.
PUB
 
[7]
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992456
Dörfler, T., et al., 2003. Working memory load and EEG coherence. Brain Topography, 15(4), p 269.
PUB
 
[6]
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992466
Gruhn, V., et al., 2003. A distributed logistic support communication system. In H. Linger, et al., eds. Proceedings of ISD 2003 - Constructing the Infrastructure for the Knowledge Economy - Methods and Tools, Theory and Practice. London: Kluwer Academic Publishers, pp. 705-713.
PUB
 
[5]
2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992544
Schleif, F.-M., & Stamer, H., 2002. {LaTeX} im studentischen Alltag. Gaotenblatt, , p 3-10.
PUB
 
[4]
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992483
Köhler, M., et al., 2002. Complexity and difficulty in memory based comparison. In J. A. da Silva, N. P. R. Filho, & E. H. Matsushima, eds. Proceedings of the 18th Meeting of the International Society for Psychophysics. Pabst Publishing, pp. 433-439.
PUB
 
[3]
2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992515
Schleif, F.-M., 2002. OCR mit statistischen Momenten. Gaotenblatt, 2002, p 15-17.
PUB
 
[2]
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992560
Simmel, A., et al., 2001. An analysis of connections between internal and external learning process indicators using EEG coherence analysis. In Proceedings of the 17th Meeting of the International Society for Psychophysics. Pabst Publishing, pp. 602-607.
PUB
 
[1]
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992461
Dörfler, T., et al., 2001. Complexity - dependent synchronization of brain subsystems during memorization. In Proceedings of the 17th Meeting of the International Society for Psychophysics. Pabst Publishing, pp. 343-348.
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[129]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031
Mokbel, B., et al., 2015. Metric learning for sequences in relational LVQ. Neurocomputing, 169(SI), p 306-322.
PUB | PDF | DOI | Download (ext.) | WoS
 
[128]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2910619
Schleif, F.-M., Villmann, T., & Zhu, X., 2015. High Dimensional Matrix Relevance Learning. In 2014 IEEE International Conference on Data Mining Workshop. Piscataway, NJ: IEEE.
PUB | DOI
 
[127]
2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2910885
Schleif, F.-M., Gisbrecht, A., & Tino, P., 2015. Large Scale Indefinite Kernel Fisher Discriminant. In A. Feragen, M. Pelillo, & M. Loog, eds. Similarity-Based Pattern Recognition. Similarity-Based Pattern Recognition : Third International Workshop, SIMBAD 2015, Proceedings. Lecture Notes in Computer Science. no.9370 Cham: Springer International Publishing, pp. 160-170.
PUB | DOI
 
[126]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772422
Gisbrecht, A., & Schleif, F.-M., 2015. Metric and non-metric proximity transformations at linear costs. Neurocomputing, 167, p 643-657.
PUB | DOI | WoS
 
[125]
2015 | Zeitschriftenaufsatz | Veröffentlicht | 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), p 95-116.
PUB | DOI | WoS
 
[124]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
Hofmann, D., et al., 2014. Learning interpretable kernelized prototype-based models. Neurocomputing, 141, p 84-96.
PUB | DOI | Download (ext.) | WoS
 
[123]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730
Hammer, B., et al., 2014. Learning vector quantization for (dis-)similarities. NeuroComputing, 131, p 43-51.
PUB | DOI | WoS
 
[122]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2690490
Strickert, M., et al., 2014. Correlation-based embedding of pairwise score data. Neurocomputing, 141, p 97-109.
PUB | DOI | WoS
 
[121]
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2672504
Zhu, X., Schleif, F.-M., & Hammer, B., 2014. Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data. Pattern Recognition Lettters, 49, p 138-145.
PUB | DOI | WoS
 
[120]
2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2612731
Micheli, A., Schleif, F.-M., & Tino, P., 2013. Novel approaches in machine learning and computational intelligence. Neurocomputing, 112, p 1-3.
PUB | DOI | WoS
 
[119]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625202
Schleif, F.-M., Zhu, X., & Hammer, B., 2013. Sparse prototype representation by core sets. In et.al Hujun Yin, ed. IDEAL 2013.
PUB
 
[118]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615717
Zhu, X., Schleif, F.-M., & Hammer, B., 2013. Secure Semi-supervised Vector Quantization for Dissimilarity Data. In I. Rojas, G. Joya, & J. Cabestany, eds. IWANN (1). Lecture Notes in Computer Science. no.7902 Springer, pp. 347-356.
PUB | DOI
 
[117]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615724
Schleif, F.-M., & Gisbrecht, A., 2013. Data Analysis of (Non-)Metric Proximities at Linear Costs. In Proceedings of SIMBAD 2013. Springer, pp. 59-74.
PUB | DOI
 
[116]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615701
Zhu, X., Schleif, F.-M., & Hammer, B., 2013. Semi-Supervised Vector Quantization for proximity data. In Proceedings of ESANN 2013. pp. 89-94.
PUB
 
[115]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625232
Gisbrecht, A., et al., 2012. Linear Time Relational Prototype Based Learning. Int. J. Neural Syst., 22(5).
PUB | DOI | WoS | PubMed | Europe PMC
 
[114]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615745
Bunte, K., Schleif, F.-M., & Biehl, M., 2012. Adaptive Learning for complex-valued data. In Proceedings of ESANN 2012. pp. 387-392.
PUB
 
[113]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534898
Biehl, M., et al., 2012. Large margin linear discriminative visualization by Matrix Relevance Learning. In IJCNN. pp. 1-8.
PUB | DOI
 
[112]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615750
Schleif, F.-M., et al., 2012. Fast approximated relational and kernel clustering. In Proceedings of ICPR 2012. IEEE, pp. 1229-1232.
PUB
 
[111]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615756
Schleif, F.-M., Zhu, X., & Hammer, B., 2012. Soft Competitive Learning for large data sets. In Proceedings of MCSD 2012. pp. 141-151.
PUB | DOI
 
[110]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2534839
Gisbrecht, A., et al., 2012. Linear Time Relational Prototype Based Learning. Int. J. Neural Syst., 22(05), p 1250021.
PUB | DOI | WoS | PubMed | Europe PMC
 
[109]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534877
Schleif, F.-M., et al., 2012. Learning Relevant Time Points for Time-Series Data in the Life Sciences. In ICANN (2). Lecture Notes in Computer Science. no.7553 pp. 531-539.
PUB | DOI
 
[108]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
Bunte, K., et al., 2012. Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks, 26, p 159-173.
PUB | DOI | WoS | PubMed | Europe PMC
 
[107]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534888
Schleif, F.-M., Zhu, X., & Hammer, B., 2012. A Conformal Classifier for Dissimilarity Data. In AIAI (2). pp. 234-243.
PUB | DOI
 
[106]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534910
Zhu, X., Schleif, F.-M., & Hammer, B., 2012. Patch Processing for Relational Learning Vector Quantization. In ISNN (1). pp. 55-63.
PUB | DOI
 
[105]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534868
Hammer, B., et al., 2012. White Box Classification of Dissimilarity Data. In HAIS (1). pp. 309-321.
PUB | DOI | WoS
 
[104]
2012 | Konferenzbeitrag | Veröffentlicht | 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. pp. 1-8.
PUB | DOI
 
[103]
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509852
Zhu, X., et al., 2012. Approximation techniques for clustering dissimilarity data. Neurocomputing, 90, p 72-84.
PUB | DOI | WoS
 
[102]
2011 | Preprint | Veröffentlicht | PUB-ID: 2534994
Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2011. Supervised learning of short and high-dimensional temporal sequences for life science measurements.
PUB | arXiv
 
[101]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276480
Gisbrecht, A., et al., 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. no.6936 Berlin, Heidelberg: Springer, pp. 25-33.
PUB | DOI
 
[100]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276485
Hammer, B., et al., 2011. Topographic Mapping of Dissimilarity Data. In WSOM'11.
PUB
 
[99]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276492
Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2011. Accelerating Kernel Neural Gas. In S. Kaski, et al., eds. ICANN'2011.
PUB
 
[98]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276644
Seiffert, U., Schleif, F.-M., & Zühlke, D., 2011. Recent Trends in Computational Intelligence in Life Science. In Proceedings of ESANN 2011. pp. 77-86.
PUB
 
[97]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276640
Bunte, K., Schleif, F.-M., & Villmann, T., 2011. Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. In Proceedings of ESANN 2011. Ciaco - i6doc.com, pp. 29-34.
PUB
 
[96]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2290045
Lee, J.A., Schleif, F.-M., & Martinetz, T., 2011. Advances in artificial neural networks, machine learning, and computational intelligence. Neurocomputing, 74(9), p 1299-1300.
PUB | DOI | WoS
 
[95]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2309980
Schleif, F.-M., et al., 2011. Efficient Kernelized Prototype-based Classification. International Journal of Neural Systems, 21(06), p 443-457.
PUB | DOI | WoS | PubMed | Europe PMC
 
[94]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276522
Gisbrecht, A., et al., 2011. Accelerating dissimilarity clustering for biomedical data analysis. In IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp. pp.154-161.
PUB
 
[93]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276654
Schleif, F.-M., 2011. Sparse Kernel Vector Quantization with Local Dependencies. In Proceedings of IJCNN 2011. pp. accepted.
PUB
 
[92]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992489
Mwebaze, E., et al., 2011. Divergence based classification in Learning Vector Quantization. Neurocomputing, 74(9), p 1429-1435.
PUB | DOI | WoS
 
[91]
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2094556
Schleif, F.-M., et al., 2011. Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications. Bioinformatics, 27(4), p 524-533.
PUB | DOI | WoS | PubMed | Europe PMC
 
[90]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276636
Schleif, F.-M., Simmuteit, S., & Villmann, T., 2011. Hierarchical deconvolution of linear mixtures of high-dimensional mass spectra in micro-biology. In Proceedings of AIA 2011. pp. in press.
PUB
 
[89]
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276648
Schneider, P., et al., 2011. Multivariate class labeling in Robust Soft LVQ. In Proceedings of ESANN 2011. pp. 17-22.
PUB
 
[88]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276626
Simmuteit, S., Schleif, F.-M., & Villmann, T., 2010. Hierarchical evolving trees together with global and local learning for large data sets in MALDI imaging. In Proceedings of WCSB 2010. pp. 103-106.
PUB
 
[87]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994127
Villmann, T., et al., 2010. Divergence Based Online Learning in Vector Quantization. In L. Rutkowski, et al., eds. Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Berlin, Heidelberg: Springer, pp. 479-486.
PUB | DOI
 
[86]
2010 | Konferenzbeitrag | Im Druck | PUB-ID: 1992498
Mwebaze, E., et al., In Press. Divergence based Learning Vector Quantization. In Proceedings of ESANN 2010.
PUB
 
[85]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276630
Schleif, F.-M., et al., 2010. Efficient identification and quantification of metabolites in 1-H NMR measurements by a novel data encoding approach. In Proceedings of WCSB 2010. pp. 91-94.
PUB
 
[84]
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992441
Angulo, C., Lee, J.A., & Schleif, F.-M., 2010. Advances in computational intelligence and learning. NeuroComputing, 73(7-9), p 1049-1050.
PUB | DOI | WoS
 
[83]
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993978
Schleif, F.-M., et al., 2010. Generalized derivative based Kernelized learning vector quantization. In C. Fyfe, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Berlin u.a.: Springer, pp. 21-28.
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2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994034
Simmuteit, S., et al., 2010. Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowledge and Information Systems, 25(2), p 327-343.
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2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992623
Zühlke, D., et al., 2010. Learning vector quantization for heterogeneous structured data. In Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN) 2010. Evere, Belgium: d-side publications.
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2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994138
Villmann, T., et al., 2010. The Mathematics of Divergence Based Online Learning in Vector Quanitzation. In N. El Gayar & F. Schwenker, eds. ANNPR'2010. Berlin, Heidelberg: Springer, pp. 108-119.
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2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994227
Villmann, T., Schleif, F.-M., & Hammer, B., 2010. Sparse representation of data. In M. Verleysen, ed. ESANN'10. D side, pp. 225-234.
PUB
 
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2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993984
Schleif, F.-M., et al., 2009. Cancer Informatics by Prototype-networks in Mass Spectrometry. Artificial Intelligence in Medicine, 45(2-3), p 215-228.
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2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992551
Schleif, F.-M., & Villmann, T., 2009. Neural Maps and Learning Vector Quantization - Theory and Applications. In Proceedings of the ESANN 2009. European Symposium on Artificial Neural Networks. Advances in Computational Intelligence and Learning. Evere, Belgium: d-side publications, pp. 509-516.
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2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992570
Simmuteit, S., et al., 2009. Hierarchical PCA using Tree-SOM for the Identification of Bacteria. In J. C. Príncipe & R. Miikkulainen, eds. Advances in Self-Organizing Maps. Proceedings of the 7th International Workshop on Self Organizing Maps WSOM 2009. LNCS, 5629. Berlin: Springer, pp. 272-280.
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2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992575
Simmuteit, S., et al., 2009. Deconvolution and Identification of Mass Spectra from mixed and pure colonies of bacteria. In J. Blazewicz, K. Ecker, & B. Hammer, eds. ICOLE 2009. IfI-09-12. Clausthal-Zellerfeld, Germany: Technical University of Clausthal, pp. 104-112.
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2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992607
Villmann, T., & Schleif, F.-M., 2009. Functional Vector Quantization by Neural Maps. In Proceedings of Whispers 2009.
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[73]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994067
Strickert, M., et al., 2009. Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. In M. Biehl, et al., eds. Similarity-based Clustering. LNAI, 5400. Berlin: Springer, pp. 70-91.
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2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992547
Schleif, F.-M., Biehl, M., & Vellido, A., 2009. Advances in machine learning and computational intelligence. NeuroComputing, 72(7-9), p 1377-1378.
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2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992580
Strickert, M., et al., 2009. Matrix metric adaptation for improved linear discriminant analysis of biomedical data. In J. Cabestany, et al., eds. Bio-Inspired Systems: Computational and Ambient Intelligence, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. LNCS, 5517. no.Part 1 Berlin: Springer, pp. 933-940.
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[70]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992526
Schleif, F.-M., Villmann, T., & Ongyerth, M., 2009. Supervised data analysis and reliability estimation for spectral data. NeuroComputing, 72(16-18), p 3590-3601.
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[69]
2009 | Report | Veröffentlicht | PUB-ID: 1993316
Biehl, M., et al., 2009. Stationarity of Matrix Relevance Learning Vector Quantization, Machine Learning Reports, Leipzig: Universität Leipzig.
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[68]
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992534
Schleif, F.-M., et al., 2009. Extended Targeted Profiling to Identify and Quantify Metabolites in 1-H NMR measurements. In J. Blazewicz, K. Ecker, & B. Hammer, eds. ICOLE 2009. IfI-09-12. Clausthal-Zellerfeld, Germany: Technical University of Clausthal, pp. 89-103.
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2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992565
Simmuteit, S., et al., 2009. Tanimoto metric in Tree-SOM for improved representation of mass spectrometry data with an underlying taxonomic structure. In Proceedings of ICMLA 2009. IEEE Press, pp. 563--567.
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[66]
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992517
Schleif, F.-M., et al., 2009. Support Vector Classification of Proteomic Profile Spectra based on Feature Extraction with the Bi-orthogonal Discrete Wavelet Transform. Computing and Visualization in Science, 12(4), p 189-199.
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[65]
2008 | Sammelwerksbeitrag | Veröffentlicht | 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 J. R. -n R. -al Dopico, J. Dorado, & A. Pazos, eds. Encyclopedia of Artificial Intelligence. IGI Global, pp. 1337-1342.
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2008 | Report | Veröffentlicht | PUB-ID: 1993379
Bunte, K., et al., 2008. Discriminative Visualization by Limited Rank Matrix Learning, Machine Learning Reports, Leipzig: Universität Leipzig.
PUB
 
[63]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992530
Schleif, F.-M., Ongyerth, M., & Villmann, T., 2008. Sparse coding Neural Gas for analysis of Nuclear Magnetic Resonance Spectroscopy. In Proceedings of the CBMS 2008. pp. 620-625.
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[62]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992554
Schneider, P., et al., 2008. Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data. In M. Verleysen, ed. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008. Evere, Belgium: d-side publications, pp. 451-456.
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2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992597
Strickert, M., Schleif, F.-M., & Villmann, T., 2008. Metric adaptation for supervised attribute rating. In M. Verleysen, ed. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008. Evere, Belgium: d-side publications, pp. 31-36.
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2008 | Zeitschriftenaufsatz | Veröffentlicht | 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), p 4-16.
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[59]
2008 | Sammelwerksbeitrag | Veröffentlicht | 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 M. Van de Werff, A. Delder, & R. Tollenaar, eds. Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Berlin: Springer, pp. 141-167.
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[58]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992539
Schleif, F.-M., et al., 2008. Automatic Identification and Quantification of Metabolites in H-NMR Measurements. In Proceedings of the Workshop on Computational Systems Biology (WCSB) 2008. pp. 165-168.
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2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992589
Strickert, M., Schleif, F.-M., & Seiffert, U., 2008. Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Ibero-American Journal of Artificial Intelligence, 37(12), p 37-44.
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2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994253
Villmann, T., et al., 2008. Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics, 9(2), p 129-143.
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[55]
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2017617
Villmann, T., et al., 2008. Fuzzy Classification Using Information Theoretic Learning Vector Quantization. Neurocomputing, 71(16-18), p 3070-3076.
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[54]
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
Geweniger, T., et al., 2008. Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity. In M. Köppen, N. K. Kasabov, & G. G. Coghill, eds. ICONIP 2008. Berlin, Heidelberg: Springer, pp. 61-69.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267
Villmann, T., et al., 2007. Class imaging of hyperspectral satellite remote sensing data using FLSOM. In Proceedings of 6th International Workshop on Self-Organizing Maps. Bielefeld: Bielefeld University.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016
Schneider, P., et al., 2007. Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data. In Proceedings of 6th International Workshop on Self-Organizing Maps. Bielefeld: Bielefeld University.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993547
Hammer, B., et al., 2007. Intuitive Clustering of Biological Data. In Proceedings of International Joint Conference on Neural Networks. IEEE, pp. 1877-1882.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993852
Schleif, F.-M., 2007. Advances in pre-processing and model generation for mass spectrometric data analysis. In M. Biehl, et al., eds. Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings. Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany.
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2007 | Report | Veröffentlicht | 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.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992602
Strickert, M., et al., 2007. Derivatives of Pearson Correlation for Gradient based Analysis of Biomedical Data. In Similarity based Clustering. Lecture Notes in Artificial Intelligence, 5400.
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2007 | Konferenzbeitrag | Veröffentlicht | 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 F. Masulli, S. Mitra, & G. Pasi, eds. Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Berlin, Heidelberg: Springer, pp. 563-570.
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2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992509
Schleif, F.-M., 2007. Prototypen basiertes maschinelles Lernen in der klinischen Proteomik. In D. Wagner, ed. Ausgezeichnete Informatikdissertationen 2006. GI-Edition Lecture Notes in Informatics. Dissertation. no.7 Bonn: Gesellschaft für Informatik, pp. 179-188.
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2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993911
Schleif, F.-M., Hammer, B., & Villmann, T., 2007. Margin based Active Learning for LVQ Networks. Neurocomputing, 70(7-9), p 1215-1224.
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[44]
2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992507
Schleif, F.-M., 2007. Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik. KI - Künstliche Intelligenz, (4), p 65-67.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992610
Villmann, T., et al., 2007. Association learning in SOMs for Fuzzy-Classification. In 6th International Conference on Machine Learning and Applications, 2007. pp. 581-586.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993811
Hasenfuss, A., et al., 2007. Neural gas clustering for dissimilarity data with continuous prototypes. In F. Sandoval, et al., eds. Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Berlin: Springer, pp. 539-546.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992616
Villmann, T., et al., 2007. Visualization of fuzzy information in in fuzzy-classification for image sagmentation using MDS. In M. Verleysen, ed. Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN) 2007. Evere, Belgium: d-side publications, pp. 103-108.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992452
Deininger, S.-O., Gerhard, M., & Schleif, F.-M., 2007. Statistical Classification and Visualization of MALDI-Imaging Data. In Proc. of CBMS 2007. pp. 403-405.
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2007 | Report | Veröffentlicht | PUB-ID: 1992505
Schleif, F.-M., 2007. Preprocessing of Nuclear Magnetic Resonance Spectrometry Data, Machine Learning Reports, Leipzig: Universität Leipzig.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992586
Strickert, M., & Schleif, F.-M., 2007. Supervised Attribute Relevance Determination for Protein Identification in Stress Experiments. In Proc. of MLSB 2007. pp. 81-86.
PUB
 
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992593
Strickert, M., Schleif, F.-M., & Seiffert, U., 2007. Gradients of Pearson Correlation for Analysis of Biomedical Data. In Proc. of ASAI 2007. pp. 139-150.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993820
Hasenfuss, A., et al., 2007. Neural gas clustering for sparse proximity data. In F. Sandoval, et al., eds. Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Berlin, Heidelberg, Germany: Springer, pp. 539-546.
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2007 | Konferenzbeitrag | Veröffentlicht | 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 F. Sandoval, et al., eds. Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Berlin, Heidelberg: Springer, pp. 1036-1044.
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994258
Villmann, T., et al., 2007. Fuzzy Labeled Self Organizing Map for Clasification of Spectra. In F. Sandoval, et al., eds. Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Berlin: Springer, pp. 556-563.
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993578
Hammer, B., et al., 2006. Supervised Batch Neural Gas. In F. Schwenker, ed. Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR). Berlin: Springer Verlag, pp. 33-45.
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993895
Schleif, F.-M., Hammer, B., & Villmann, T., 2006. Margin based Active Learning for LVQ Networks. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks. Brussels, Belgium: d-side publications, pp. 539-544.
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994184
Villmann, T., et al., 2006. Prototype based classification using information theoretic learning. In I. King, et al., eds. Neural Information Processing, 13th International Conference. Proceedings. Lecture Notes in Computer Science, 4233. no.Part II Berlin: Springer, pp. 40-49.
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994273
Villmann, T., et al., 2006. Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. In F. Schwenker, ed. Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Berlin: Springer, pp. 46-56.
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2006 | Dissertation | PUB-ID: 1992511
Schleif, F.-M., 2006. Prototype based Machine Learning for Clinical Proteomics, Clausthal-Zellerfeld, Germany: Technical University Clausthal.
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993889
Schleif, F.-M., et al., 2006. Machine Learning and Soft-Computing in Bioinformatics. A Short Journey. In Proc. of FLINS 2006. World Scientific Press, pp. 541-548.
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993568
Hammer, B., et al., 2006. Supervised median neural gas. In C. Dagli, et al., eds. Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. ASME Press, pp. 623-633.
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
Hammer, B., et al., 2006. Supervised median clustering. In C. H. Dagli, ed. 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). ASME Press series on intelligent engineering systems through artificial neural networks, 16. New York, NY: ASME Press, pp. 623-632.
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
Schleif, F.-M., et al., 2006. Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps. In D. J. Lee, et al., eds. 19th IEEE International Symposium on Computer- based Medical Systems. Los Alamitos: IEEE Computer Society Press, pp. 919-924.
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2006 | Zeitschriftenaufsatz | Veröffentlicht | 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), p 610-622.
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992445
Brüß, C., et al., 2006. Fuzzy Image Segmentation with Fuzzy Labelled Neural Gas. In Proc. of ESANN 2006. pp. 563-569.
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2006 | Report | Veröffentlicht | PUB-ID: 1993584
Hammer, B., et al., 2006. Supervised median clustering, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology.
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2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
Villmann, T., et al., 2006. Fuzzy Classification by Fuzzy Labeled Neural Gas. Neural Networks, 19(6-7), p 772-779.
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2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994241
Villmann, T., Schleif, F.-M., & Hammer, B., 2006. Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing, 69(16-18), p 2425-2428.
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
Hammer, B., et al., 2006. Learning vector quantization classification with local relevance determination for medical data. In L. Rutkowski, et al., eds. Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Berlin, Heidelberg: Springer, pp. 603-612.
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2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
Villmann, T., et al., 2005. Fuzzy Labeled Neural GAS for Fuzzy Classification. In M. Cottrell, ed. Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Paris, France: University Paris-1-Pantheon-Sorbonne, pp. 283-290.
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2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992513
Schleif, F.-M., 2005. Plugins mit wxWidgets. Offene Systeme, 2005(1), p 5-10.
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2005 | Konferenzbeitrag | Veröffentlicht | 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.
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2005 | Report | Veröffentlicht | 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.
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2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993974
Schleif, F.-M., Villmann, T., & Hammer, B., 2005. Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. In I. Bloch, A. Petrosino, & A. G. B. Tettamanzi, eds. Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Berlin, Heidelberg: Springer, pp. 290-296.
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2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994249
Villmann, T., Schleif, F.-M., & Hammer, B., 2005. Fuzzy labeled soft nearest neighbor classification with relevance learning. In M. A. Wani, K. J. Cios, & K. Hafeez, eds. Proceedings of the International Conference of Machine Learning Applications. Los Angeles: IEEE Press, pp. 11-15.
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2004 | Konferenzbeitrag | Veröffentlicht | 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 H. - M. Groß, K. Debes, & H. - J. Böhme, eds. Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. VDI Verlag, pp. 592-597.
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2004 | Konferenzbeitrag | Veröffentlicht | 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 H. - M. Groß, K. Debes, & H. - J. Böhme, eds. SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. VDI Verlag.
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2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
Schleif, F.-M., et al., 2004. Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data. In M. A. Wani, K. J. Cios, & K. Hafeez, eds. Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Los Alamitos, CA, USA: IEEE Press, pp. 374-379.
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2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994223
Villmann, T., Schleif, F.-M., & Hammer, B., 2003. Supervised Neural Gas and Relevance Learning in Learning Vector Quantization. In T. Yamakawa, ed. Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology, pp. 47-52.
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2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992477
Köhler, M., et al., 2003. A mission for the EEG coherence analysis: Is the task complex or difficult? Brain Topography, 15(4), p 271.
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2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992456
Dörfler, T., et al., 2003. Working memory load and EEG coherence. Brain Topography, 15(4), p 269.
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2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992466
Gruhn, V., et al., 2003. A distributed logistic support communication system. In H. Linger, et al., eds. Proceedings of ISD 2003 - Constructing the Infrastructure for the Knowledge Economy - Methods and Tools, Theory and Practice. London: Kluwer Academic Publishers, pp. 705-713.
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2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992544
Schleif, F.-M., & Stamer, H., 2002. {LaTeX} im studentischen Alltag. Gaotenblatt, , p 3-10.
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2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992483
Köhler, M., et al., 2002. Complexity and difficulty in memory based comparison. In J. A. da Silva, N. P. R. Filho, & E. H. Matsushima, eds. Proceedings of the 18th Meeting of the International Society for Psychophysics. Pabst Publishing, pp. 433-439.
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2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992515
Schleif, F.-M., 2002. OCR mit statistischen Momenten. Gaotenblatt, 2002, p 15-17.
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2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992560
Simmel, A., et al., 2001. An analysis of connections between internal and external learning process indicators using EEG coherence analysis. In Proceedings of the 17th Meeting of the International Society for Psychophysics. Pabst Publishing, pp. 602-607.
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2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992461
Dörfler, T., et al., 2001. Complexity - dependent synchronization of brain subsystems during memorization. In Proceedings of the 17th Meeting of the International Society for Psychophysics. Pabst Publishing, pp. 343-348.
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