Please note: This is a beta version of the new dblp website.
You can find the classic dblp view of this page here.
You can find the classic dblp view of this page here.
Thomas Villmann
2010 – today
- 2013
[j41]Derong Liu, Charles Anderson, Ahmad Taher Azar, Giorgio Battistelli, Eduardo Bayro-Corrochano, Cristiano Cervellera, David A. Elizondo, Maurizio Filippone, Giorgio Gnecco, Xiaolin Hu, Tingwen Huang, Weifeng Liu, Wenlian Lu, Ana Maria Madureira, Igor Skrjanc, Thomas Villmann, Jonathan Wu, Shengli Xie, Dong Xu: Editorial A Successful Change From TNN to TNNLS and a Very Successful Year. IEEE Trans. Neural Netw. Learning Syst. 24(1): 1-7 (2013)- [c93]Marika Kästner, Martin Riedel, Marc Strickert, Wieland Hermann, Thomas Villmann: Border-Sensitive Learning in Kernelized Learning Vector Quantization. IWANN (1) 2013: 357-366
- 2012
- [j40]Kerstin Bunte, Sven Haase, Michael Biehl, Thomas Villmann: Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. Neurocomputing 90: 23-45 (2012)
- [j39]Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann: Functional relevance learning in generalized learning vector quantization. Neurocomputing 90: 85-95 (2012)
- [j38]Kerstin Bunte, Petra Schneider, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann, Michael Biehl: Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks 26: 159-173 (2012)
- [c92]Marika Kästner, Thomas Villmann: Fuzzy Supervised Self-Organizing Map for Semi-supervised Vector Quantization. ICAISC (1) 2012: 256-265
- [c91]Thomas Villmann, Tina Geweniger, Marika Kästner, Mandy Lange: Fuzzy Neural Gas for Unsupervised Vector Quantization. ICAISC (1) 2012: 350-358
- [c90]Marika Kästner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann: Differentiable Kernels in Generalized Matrix Learning Vector Quantization. ICMLA (1) 2012: 132-137
- [c89]Thomas Villmann, Marika Kästner, David Nebel, Martin Riedel: ICMLA Face Recognition Challenge - Results of the Team Computational Intelligence Mittweida. ICMLA (2) 2012: 592-595
- [c88]Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann: Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN 2012: 1-8
- [c87]Gabriele Peters, Kerstin Bunte, Marc Strickert, Michael Biehl, Thomas Villmann: Visualization of processes in self-learning systems. PST 2012: 244-249
- 2011
- [j37]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider: Efficient Kernelized Prototype Based Classification. Int. J. Neural Syst. 21(6): 443-457 (2011)
- [j36]Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller: Neighbor embedding XOM for dimension reduction and visualization. Neurocomputing 74(9): 1340-1350 (2011)
- [j35]Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Jennifer R. Aduwo, John A. Quinn, Sven Haase, Thomas Villmann, Michael Biehl: Divergence-based classification in learning vector quantization. Neurocomputing 74(9): 1429-1435 (2011)
- [j34]Thomas Villmann, Sven Haase: Divergence-Based Vector Quantization. Neural Computation 23(5): 1343-1392 (2011)
- [c86]Kerstin Bunte, Frank-Michael Schleif, Sven Haase, Thomas Villmann: Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. ESANN 2011
- [c85]Tina Geweniger, Marika Kästner, Thomas Villmann: Optimization of Parametrized Divergences in Fuzzy c-Means. ESANN 2011
- [c84]Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann: Generalized functional relevance learning vector quantization. ESANN 2011
- [c83]Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann: Multivariate class labeling in Robust Soft LVQ. ESANN 2011
- [c82]Marc Strickert, Björn Labitzke, Andreas Kolb, Thomas Villmann: Multispectral image characterization by partial generalized covariance. ESANN 2011
- [c81]Thomas Villmann, José C. Príncipe, Andrzej Cichocki: Information theory related learning. ESANN 2011
- [c80]
- [c79]Thomas Villmann, Marika Kästner: Sparse Functional Relevance Learning in Generalized Learning Vector Quantization. WSOM 2011: 79-89
- [c78]Marika Kästner, Andreas Backhaus, Tina Geweniger, Sven Haase, Udo Seiffert, Thomas Villmann: Relevance Learning in Unsupervised Vector Quantization Based on Divergences. WSOM 2011: 90-100
- [i1]Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, Thomas Villmann: Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). Dagstuhl Reports 1(8): 67-95 (2011)
- 2010
- [j33]Tina Geweniger, Dietlind Zühlke, Barbara Hammer, Thomas Villmann: Median fuzzy c-means for clustering dissimilarity data. Neurocomputing 73(7-9): 1109-1116 (2010)
- [j32]Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowl. Inf. Syst. 25(2): 327-343 (2010)
- [j31]Petra Schneider, Kerstin Bunte, Han Stiekema, Barbara Hammer, Thomas Villmann, Michael Biehl: Regularization in matrix relevance learning. IEEE Transactions on Neural Networks 21(5): 831-840 (2010)
- [c77]Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer, Michael Biehl: The Mathematics of Divergence Based Online Learning in Vector Quantization. ANNPR 2010: 108-119
- [c76]Andreas Schierwagen, Thomas Villmann, Alán Alpár, Ulrich Gärtner: Cluster Analysis of Cortical Pyramidal Neurons Using SOM. ANNPR 2010: 120-130
- [c75]Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller: Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. ESANN 2010
- [c74]Tina Geweniger, Thomas Villmann: Extending FSNPC to handle data points with fuzzy class assignments. ESANN 2010
- [c73]Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl: Divergence based Learning Vector Quantization. ESANN 2010
- [c72]
- [c71]Dietlind Zühlke, Frank-Michael Schleif, Tina Geweniger, Sven Haase, Thomas Villmann: Learning vector quantization for heterogeneous structured data. ESANN 2010
- [c70]Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer: Divergence Based Online Learning in Vector Quantization. ICAISC (1) 2010: 479-486
- [c69]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl: Generalized Derivative Based Kernelized Learning Vector Quantization. IDEAL 2010: 21-28
2000 – 2009
- 2009
- [j30]Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa, Barbara Hammer, Alexander Gammerman: Cancer informatics by prototype networks in mass spectrometry. Artificial Intelligence in Medicine 45(2-3): 215-228 (2009)
- [j29]Frank-Michael Schleif, Thomas Villmann, Matthias Ongyerth: Supervised data analysis and reliability estimation with exemplary application for spectral data. Neurocomputing 72(16-18): 3590-3601 (2009)
- [c68]Thomas Villmann, Barbara Hammer, Michael Biehl: Some Theoretical Aspects of the Neural Gas Vector Quantizer. Similarity-Based Clustering 2009: 23-34
- [c67]Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert: Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. Similarity-Based Clustering 2009: 70-91
- [c66]Tina Geweniger, Dietlind Zühlke, Barbara Hammer, Thomas Villmann: Median Variant of Fuzzy c-Means. ESANN 2009
- [c65]Frank-Michael Schleif, Thomas Villmann: Neural Maps and Learning Vector Quantization - Theory and Applications. ESANN 2009
- [c64]Dietlind Zühlke, Tina Geweniger, Ulrich Heimann, Thomas Villmann: Fuzzy Fleiss-kappa for Comparison of Fuzzy Classifiers. ESANN 2009
- [c63]Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Thomas Elssner: Tanimoto Metric in Tree-SOM for Improved Representation of Mass Spectrometry Data with an Underlying Taxonomic Structure. ICMLA 2009: 563-567
- [c62]Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl: Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. IWANN (1) 2009: 933-940
- [c61]Tina Geweniger, Dietlind Zühlke, Barbara Hammer, Thomas Villmann: Fuzzy Variant of Affinity Propagation in Comparison to Median Fuzzy c-Means. WSOM 2009: 72-79
- [c60]Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa: Hierarchical PCA Using Tree-SOM for the Identification of Bacteria. WSOM 2009: 272-280
- [c59]Thomas Villmann, Barbara Hammer: Functional Principal Component Learning Using Oja's Method and Sobolev Norms. WSOM 2009: 325-333
- [p2]Michael Biehl, Barbara Hammer, Petra Schneider, Thomas Villmann: Metric Learning for Prototype-Based Classification. Innovations in Neural Information Paradigms and Applications 2009: 183-199
- [e2]Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann (Eds.): Similarity-Based Clustering, Recent Developments and Biomedical Applications [outcome of a Dagstuhl Seminar]. Lecture Notes in Computer Science 5400, Springer 2009, ISBN 978-3-642-01804-6
- [r1]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Prototype Based Classification in Bioinformatics. Encyclopedia of Artificial Intelligence 2009: 1337-1342
- 2008
- [j28]Marc Strickert, Frank-Michael Schleif, Udo Seiffert, Thomas Villmann: Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial 12(37): 37-44 (2008)
- [j27]Thomas Villmann, Frank-Michael Schleif, Markus Kostrzewa, Axel Walch, Barbara Hammer: Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics 9(2): 129-143 (2008)
- [j26]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Prototype based fuzzy classification in clinical proteomics. Int. J. Approx. Reasoning 47(1): 4-16 (2008)
- [c58]Marc Strickert, Petra Schneider, Jens Keilwagen, Thomas Villmann, Michael Biehl, Barbara Hammer: Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. ANNPR 2008: 78-89
- [c57]Marc Strickert, Nese Sreenivasulu, Thomas Villmann, Barbara Hammer: Robust Centroid-Based Clustering using Derivatives of Pearson Correlation. BIOSIGNALS (2) 2008: 197-203
- [c56]Frank-Michael Schleif, Matthias Ongyerth, Thomas Villmann: Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy. CBMS 2008: 620-625
- [c55]Marc Strickert, Frank-Michael Schleif, Thomas Villmann: Metric adaptation for supervised attribute rating. ESANN 2008: 31-36
- [c54]Alexander Hasenfuss, Barbara Hammer, Tina Geweniger, Thomas Villmann: Magnification Control in Relational Neural Gas. ESANN 2008: 325-330
- [c53]Thomas Villmann, Erzsébet Merényi, Udo Seiffert: Machine learning approches and pattern recognition for spectral data. ESANN 2008: 433-444
- [c52]Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl: Generalized matrix learning vector quantizer for the analysis of spectral data. ESANN 2008: 451-456
- [c51]Tina Geweniger, Frank-Michael Schleif, Alexander Hasenfuss, Barbara Hammer, Thomas Villmann: Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity. ICONIP (2) 2008: 61-69
- [p1]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar: Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers. Computational Intelligence in Biomedicine and Bioinformatics 2008: 141-167
- 2007
- [j25]Frank-Michael Schleif, Barbara Hammer, Thomas Villmann: Margin-based active learning for LVQ networks. Neurocomputing 70(7-9): 1215-1224 (2007)
- [j24]Barbara Hammer, Alexander Hasenfuss, Thomas Villmann: Magnification control for batch neural gas. Neurocomputing 70(7-9): 1225-1234 (2007)
- [j23]Erzsébet Merényi, Abha Jain, Thomas Villmann: Explicit Magnification Control of Self-Organizing Maps for "Forbidden" Data. IEEE Transactions on Neural Networks 18(3): 786-797 (2007)
- [c50]Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann: 07131 Summary -- Similarity-based Clustering and its Application to Medicine and Biology. Similarity-based Clustering and its Application to Medicine and Biology 2007
- [c49]Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann: 07131 Abstracts Collection -- Similarity-based Clustering and its Application to Medicine and Biology. Similarity-based Clustering and its Application to Medicine and Biology 2007
- [c48]
- [c47]Thomas Villmann, Marc Strickert, Cornelia Brüß, Frank-Michael Schleif, Udo Seiffert: Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. ESANN 2007: 103-108
- [c46]Thomas Villmann, Frank-Michael Schleif, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar: Association Learning in SOMs for Fuzzy-Classification. ICMLA 2007: 581-586
- [c45]Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann, Marc Strickert, Udo Seiffert: Intuitive Clustering of Biological Data. IJCNN 2007: 1877-1882
- [c44]Alexander Hasenfuss, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann: Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes. IWANN 2007: 539-546
- [c43]Thomas Villmann, Frank-Michael Schleif, Erzsébet Merényi, Barbara Hammer: Fuzzy Labeled Self-Organizing Map for Classification of Spectra. IWANN 2007: 556-563
- [c42]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra. IWANN 2007: 1036-1044
- [c41]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps. WILF 2007: 563-570
- [e1]Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann (Eds.): Similarity-based Clustering and its Application to Medicine and Biology, 25.03. - 30.03.2007. Dagstuhl Seminar Proceedings 07131, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2007
- 2006
- [j22]Marc Strickert, Udo Seiffert, Nese Sreenivasulu, Winfriede Weschke, Thomas Villmann, Barbara Hammer: Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis. Neurocomputing 69(7-9): 651-659 (2006)
- [j21]Thomas Villmann, Frank-Michael Schleif, Barbara Hammer: Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing 69(16-18): 2425-2428 (2006)
- [j20]Thomas Villmann, Jens Christian Claussen: Magnification Control in Self-Organizing Maps and Neural Gas. Neural Computation 18(2): 446-469 (2006)
- [j19]Thomas Villmann, Frank-Michael Schleif, Barbara Hammer: Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks 19(5): 610-622 (2006)
- [j18]Marie Cottrell, Barbara Hammer, Alexander Hasenfuss, Thomas Villmann: Batch and median neural gas. Neural Networks 19(6-7): 762-771 (2006)
- [j17]Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Wieland Hermann: Fuzzy classification by fuzzy labeled neural gas. Neural Networks 19(6-7): 772-779 (2006)
- [c40]Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann: Supervised Batch Neural Gas. ANNPR 2006: 33-45
- [c39]Thomas Villmann, Udo Seiffert, Frank-Michael Schleif, Cornelia Brüß, Tina Geweniger, Barbara Hammer: Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. ANNPR 2006: 46-56
- [c38]Thomas Villmann, Barbara Hammer, Udo Seiffert: Perspectives of Self-adapted Self-organizing Clustering in Organic Computing. BioADIT 2006: 141-159
- [c37]Frank-Michael Schleif, Thomas Elssner, Markus Kostrzewa, Thomas Villmann, Barbara Hammer: Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps. CBMS 2006: 919-924
- [c36]Barbara Hammer, Alexander Hasenfuss, Thomas Villmann: Magnification control for batch neural gas. ESANN 2006: 7-12
- [c35]Udo Seiffert, Barbara Hammer, Samuel Kaski, Thomas Villmann: Neural networks and machine learning in bioinformatics - theory and applications. ESANN 2006: 521-532
- [c34]Frank-Michael Schleif, Barbara Hammer, Thomas Villmann: Margin based Active Learning for LVQ Networks. ESANN 2006: 539-544
- [c33]Cornelia Brüß, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert: Fuzzy image segmentation with Fuzzy Labelled Neural Gas. ESANN 2006: 563-568
- [c32]Barbara Hammer, Thomas Villmann, Frank-Michael Schleif, Cornelia Albani, Wieland Hermann: Learning Vector Quantization Classification with Local Relevance Determination for Medical Data. ICAISC 2006: 603-612
- [c31]Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Tom Fischer, Marie Cottrell: Prototype Based Classification Using Information Theoretic Learning. ICONIP (2) 2006: 40-49
- 2005
- [j16]Marie Cottrell, Barbara Hammer, Thomas Villmann: New Aspects in Neurocomputing. Neurocomputing 63: 1-3 (2005)
- [j15]Jens Christian Claussen, Thomas Villmann: Magnification control in winner relaxing neural gas. Neurocomputing 63: 125-137 (2005)
- [j14]Jochen J. Steil, Gavin C. Cawley, Thomas Villmann: Trends in Neurocomputing at ESANN 2004. Neurocomputing 64: 1-4 (2005)
- [j13]Barbara Hammer, Marc Strickert, Thomas Villmann: Supervised Neural Gas with General Similarity Measure. Neural Processing Letters 21(1): 21-44 (2005)
- [j12]Barbara Hammer, Marc Strickert, Thomas Villmann: On the Generalization Ability of GRLVQ Networks. Neural Processing Letters 21(2): 109-120 (2005)
- [c30]Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann: Relevance learning for mental disease classification. ESANN 2005: 139-144
- [c29]
- [c28]Marc Strickert, Nese Sreenivasulu, Winfriede Weschke, Udo Seiffert, Thomas Villmann: Generalized Relevance LVQ with Correlation Measures for Biological Data. ESANN 2005: 331-338
- [c27]Thomas Villmann, Frank-Michael Schleif, Barbara Hammer: Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. ICMLA 2005
- [c26]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. WILF 2005: 290-296
- 2004
- [j11]
- [j10]Thomas Villmann, Beate Villmann, Volker Slowik: Evolutionary algorithms with neighborhood cooperativeness according to neural maps. Neurocomputing 57: 151-169 (2004)
- [c25]Thomas Villmann, Udo Seiffert, Axel Wismüller: Theory and applications of neural maps. ESANN 2004: 25-38
- [c24]
- [c23]Frank-Michael Schleif, U. Clauss, Thomas Villmann, Barbara Hammer: Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data. ICMLA 2004: 374-379
- 2003
- [j9]Thomas Villmann, Erzsébet Merényi, Barbara Hammer: Neural maps in remote sensing image analysis. Neural Networks 16(3-4): 389-403 (2003)
- [c22]
- [c21]Jens Christian Claussen, Thomas Villmann: Magnification Control in Winner Relaxing Neural Gas. ESANN 2003: 93-98
- 2002
- [j8]Thomas Villmann: Evolutionary algorithms using a neural network like migration scheme. Integrated Computer-Aided Engineering 9(1): 25-35 (2002)
- [j7]Thomas Villmann: Neural maps for faithful data modelling in medicine - state-of-the-art and exemplary applications. Neurocomputing 48(1-4): 229-250 (2002)
- [j6]Barbara Hammer, Thomas Villmann: Generalized relevance learning vector quantization. Neural Networks 15(8-9): 1059-1068 (2002)
- [c20]Axel Wismüller, Thomas Villmann: Exploratory Data Analysis in Medicine and Bioinformatics. ESANN 2002: 25-38
- [c19]
- [c18]Barbara Hammer, Marc Strickert, Thomas Villmann: Learning Vector Quantization for Multimodal Data. ICANN 2002: 370-376
- [c17]Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann: Rule Extraction from Self-Organizing Networks. ICANN 2002: 877-883
- [c16]Jutta Huhse, Thomas Villmann, Peter Merz, Andreas Zell: Evolution Strategy with Neighborhood Attraction Using a Neural Gas Approach. PPSN 2002: 391-400
- 2001
- [c15]
- [c14]
- [c13]Thomas Villmann, Conny Albani: Clustering of Categoric Data in Medicine - Application of Evolutionary Algorithms. Fuzzy Days 2001: 619-627
- 2000
- [c12]Thomas Villmann: Neural networks approaches in medicine - a review of actual developments. ESANN 2000: 165-176
- [c11]Thomas Villmann, Reiner Haupt, Klaus Hering: Parallel Evolutionary Algorithms with SOM-Like Migration and its Application to VLSI-Design. IJCNN (5) 2000: 167-172
- [c10]Thomas Villmann, Wieland Hermann, Michael Geyer: Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Maps. ISMDA 2000: 138-151
1990 – 1999
- 1999
- [j5]Hans-Ulrich Bauer, J. Michael Herrmann, Thomas Villmann: Neural maps and topographic vector quantization. Neural Networks 12(4-5): 659-676 (1999)
- [c9]Thomas Villmann: Benefits and limits of the self-organizing map and its variants in the area of satellite remote sensoring processing. ESANN 1999: 111-116
- 1998
- [j4]Thomas Villmann, Hans-Ulrich Bauer: Applications of the growing self-organizing map. Neurocomputing 21(1-3): 91-100 (1998)
- [c8]
- [c7]Thomas Villmann, A. Körner, Conny Albani: Evolutionary Algorithms with Self-Organizing Population Dynamic for Clustering of Categories in Psychotherapy Research Using Large Clinical Data Sets. NC 1998: 130-136
- 1997
- [j3]Ralf Der, J. Michael Herrmann, Thomas Villmann: Time behavior of topological ordering in self-organizing feature mapping. Biological Cybernetics 77(6): 419-427 (1997)
- [j2]Hans-Ulrich Bauer, Thomas Villmann: Growing a hypercubical output space in a self-organizing feature map. IEEE Trans. Neural Netw. Learning Syst. 8(2): 218-226 (1997)
- [j1]Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz: Topology preservation in self-organizing feature maps: exact definition and measurement. IEEE Trans. Neural Netw. Learning Syst. 8(2): 256-266 (1997)
- [c6]J. Michael Herrmann, Hans-Ulrich Bauer, Thomas Villmann: Measuring topology preservation in maps of real-world data. ESANN 1997
- [c5]Thomas Villmann, Beate Villmann, Conny Albani: Application of Evolutionary Algorithms to the Problem of New Clustering of Psychological Categories Using Real Clinical Data Sets. Fuzzy Days 1997: 311-320
- [c4]J. Michael Herrmann, Thomas Villmann: Vector Quantization by Optimal Neural Gas. ICANN 1997: 625-630
- 1996
- [c3]Klaus Hering, Reiner Haupt, Thomas Villmann: Hierarchical Strategy of Model Partitioning for VLSI-Design Using an Improved Mixture of Experts Approach. Workshop on Parallel and Distributed Simulation 1996: 106-113
- 1994
- [c2]Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz: Topology Preservation in Self-Organizing Feature Maps: General Definition and Efficient Measurement. Fuzzy Days 1994: 159-166
- 1993
- [c1]
Coauthor Index
[j39] [j38] [j37] [j36] [c84] [i1] [j33] [j32] [j31] [c77] [c75] [c72] [c70] [c69] [j30] [c68] [c66] [c61] [c59] [p2] [e2] [r1] [j27] [j26] [c58] [c57] [c54] [c51] [p1] [j25] [j24] [c50] [c49] [c48] [c45] [c44] [c43] [c42] [c41] [e1] [j22] [j21] [j19] [j18] [j17] [c40] [c39] [c38] [c37] [c36] [c35] [c34] [c32] [c31] [j16] [j13] [j12] [c30] [c29] [c27] [c26] [c24] [c23] [j9] [c22] [j6] [c19] [c18] [c17] [c14]
data released under the ODC-BY 1.0 license. See also our legal information page
last updated on 2013-10-02 10:57 CEST by the dblp team