- CSB - MetaPress - Google - Bing - Yahoo
 | 2013 | |
|---|---|---|
| j40 |      | 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) |
| 2012 | ||
| j39 | | 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) |
| j38 | | Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann: Functional relevance learning in generalized learning vector quantization. Neurocomputing 90: 85-95 (2012) |
| j37 | | 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 | ||
| j36 | | Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider: Efficient Kernelized Prototype Based Classification. Int. J. Neural Syst. 21(6): 443-457 (2011) |
| j35 | | Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller: Neighbor embedding XOM for dimension reduction and visualization. Neurocomputing 74(9): 1340-1350 (2011) |
| j34 | | 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) |
| j33 | | 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 | ||
| j32 | | Tina Geweniger, Dietlind Zühlke, Barbara Hammer, Thomas Villmann: Median fuzzy c-means for clustering dissimilarity data. Neurocomputing 73(7-9): 1109-1116 (2010) |
| j31 | | 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) |
| j30 | | 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 |
| 2009 | ||
| j29 | | 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) |
| j28 | | 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 |
| r1 | | Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Prototype Based Classification in Bioinformatics. Encyclopedia of Artificial Intelligence 2009: 1337-1342 |
| 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 |
| 2008 | ||
| j27 | | 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) |
| j26 | | 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) |
| j25 | | 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 | ||
| j24 | | Frank-Michael Schleif, Barbara Hammer, Thomas Villmann: Margin-based active learning for LVQ networks. Neurocomputing 70(7-9): 1215-1224 (2007) |
| j23 | | Barbara Hammer, Alexander Hasenfuss, Thomas Villmann: Magnification control for batch neural gas. Neurocomputing 70(7-9): 1225-1234 (2007) |
| j22 | | 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 | ||
| j21 | | 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) |
| j20 | | Thomas Villmann, Frank-Michael Schleif, Barbara Hammer: Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing 69(16-18): 2425-2428 (2006) |
| j19 | | Thomas Villmann, Jens Christian Claussen: Magnification Control in Self-Organizing Maps and Neural Gas. Neural Computation 18(2): 446-469 (2006) |
| j18 | | 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) |
| j17 | | Marie Cottrell, Barbara Hammer, Alexander Hasenfuss, Thomas Villmann: Batch and median neural gas. Neural Networks 19(6-7): 762-771 (2006) |
| j16 | | 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 | ||
| j15 | | Marie Cottrell, Barbara Hammer, Thomas Villmann: New Aspects in Neurocomputing. Neurocomputing 63: 1-3 (2005) |
| j14 | | Jens Christian Claussen, Thomas Villmann: Magnification control in winner relaxing neural gas. Neurocomputing 63: 125-137 (2005) |
| j13 | | Jochen J. Steil, Gavin C. Cawley, Thomas Villmann: Trends in Neurocomputing at ESANN 2004. Neurocomputing 64: 1-4 (2005) |
| j12 | | Barbara Hammer, Marc Strickert, Thomas Villmann: Supervised Neural Gas with General Similarity Measure. Neural Processing Letters 21(1): 21-44 (2005) |
| j11 | | 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 | ||
| j10 | | |
| j9 | | 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 | ||
| j8 | | 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 | ||
| 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, R. 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 |
| 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, R. 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 | | |
Colors in the list of coauthors
Last update Sun May 19 09:43:53 2013 CET by the DBLP Team —
Data released under the ODC-BY 1.0 license — See also our legal information page