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Thomas Villmann: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Thomas Villmann, Barbara Hammer, Udo Seiffert
    Perspectives of Self-adapted Self-organizing Clustering in Organic Computing. [Citation Graph (0, 0)][DBLP]
    BioADIT, 2006, pp:141-159 [Conf]
  2. Frank-Michael Schleif, Thomas Elssner, Markus Kostrzewa, Thomas Villmann, Barbara Hammer
    Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps. [Citation Graph (0, 0)][DBLP]
    CBMS, 2006, pp:919-924 [Conf]
  3. Cornelia Brüß, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert
    Fuzzy image segmentation with Fuzzy Labelled Neural Gas. [Citation Graph (0, 0)][DBLP]
    ESANN, 2006, pp:563-568 [Conf]
  4. Jens Christian Claussen, Thomas Villmann
    Magnification Control in Winner Relaxing Neural Gas. [Citation Graph (0, 0)][DBLP]
    ESANN, 2003, pp:93-98 [Conf]
  5. Barbara Hammer, Alexander Hasenfuss, Thomas Villmann
    Magnification control for batch neural gas. [Citation Graph (0, 0)][DBLP]
    ESANN, 2006, pp:7-12 [Conf]
  6. Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann
    Relevance learning for mental disease classification. [Citation Graph (0, 0)][DBLP]
    ESANN, 2005, pp:139-144 [Conf]
  7. Barbara Hammer, Thomas Villmann
    Input pruning for neural gas architectures. [Citation Graph (0, 0)][DBLP]
    ESANN, 2001, pp:283-288 [Conf]
  8. Barbara Hammer, Thomas Villmann
    Batch-RLVQ. [Citation Graph (0, 0)][DBLP]
    ESANN, 2002, pp:295-300 [Conf]
  9. Barbara Hammer, Thomas Villmann
    Mathematical Aspects of Neural Networks. [Citation Graph (0, 0)][DBLP]
    ESANN, 2003, pp:59-72 [Conf]
  10. Barbara Hammer, Thomas Villmann
    Classification using non-standard metrics. [Citation Graph (0, 0)][DBLP]
    ESANN, 2005, pp:303-316 [Conf]
  11. J. Michael Herrmann, Hans-Ulrich Bauer, Thomas Villmann
    Measuring topology preservation in maps of real-world data. [Citation Graph (0, 0)][DBLP]
    ESANN, 1997, pp:- [Conf]
  12. Marc Strickert, Nese Sreenivasulu, Winfriede Weschke, Udo Seiffert, Thomas Villmann
    Generalized Relevance LVQ with Correlation Measures for Biological Data. [Citation Graph (0, 0)][DBLP]
    ESANN, 2005, pp:331-338 [Conf]
  13. Frank-Michael Schleif, Barbara Hammer, Thomas Villmann
    Margin based Active Learning for LVQ Networks. [Citation Graph (0, 0)][DBLP]
    ESANN, 2006, pp:539-544 [Conf]
  14. Udo Seiffert, Barbara Hammer, Samuel Kaski, Thomas Villmann
    Neural networks and machine learning in bioinformatics - theory and applications. [Citation Graph (0, 0)][DBLP]
    ESANN, 2006, pp:521-532 [Conf]
  15. Thomas Villmann
    Neural networks approaches in medicine - a review of actual developments. [Citation Graph (0, 0)][DBLP]
    ESANN, 2000, pp:165-176 [Conf]
  16. Thomas Villmann
    Evolutionary algorithms and neural networks in hybrid systems. [Citation Graph (0, 0)][DBLP]
    ESANN, 2001, pp:137-152 [Conf]
  17. Thomas Villmann
    Benefits and limits of the self-organizing map and its variants in the area of satellite remote sensoring processing. [Citation Graph (0, 0)][DBLP]
    ESANN, 1999, pp:111-116 [Conf]
  18. Thomas Villmann, J. Michael Herrmann
    Magnification control in neural maps. [Citation Graph (0, 0)][DBLP]
    ESANN, 1998, pp:191-196 [Conf]
  19. Thomas Villmann, Udo Seiffert, Axel Wismüller
    Theory and applications of neural maps. [Citation Graph (0, 0)][DBLP]
    ESANN, 2004, pp:25-38 [Conf]
  20. Axel Wismüller, Thomas Villmann
    Exploratory Data Analysis in Medicine and Bioinformatics. [Citation Graph (0, 0)][DBLP]
    ESANN, 2002, pp:25-38 [Conf]
  21. Thomas Villmann, Conny Albani
    Clustering of Categoric Data in Medicine - Application of Evolutionary Algorithms. [Citation Graph (0, 0)][DBLP]
    Fuzzy Days, 2001, pp:619-627 [Conf]
  22. Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz
    Topology Preservation in Self-Organizing Feature Maps: General Definition and Efficient Measurement. [Citation Graph (0, 0)][DBLP]
    Fuzzy Days, 1994, pp:159-166 [Conf]
  23. Thomas Villmann, Beate Villmann, Conny Albani
    Application of Evolutionary Algorithms to the Problem of New Clustering of Psychological Categories Using Real Clinical Data Sets. [Citation Graph (0, 0)][DBLP]
    Fuzzy Days, 1997, pp:311-320 [Conf]
  24. Barbara Hammer, Thomas Villmann, Frank-Michael Schleif, Cornelia Albani, Wieland Hermann
    Learning Vector Quantization Classification with Local Relevance Determination for Medical Data. [Citation Graph (0, 0)][DBLP]
    ICAISC, 2006, pp:603-612 [Conf]
  25. Barbara Hammer, Marc Strickert, Thomas Villmann
    Relevance LVQ versus SVM. [Citation Graph (0, 0)][DBLP]
    ICAISC, 2004, pp:592-597 [Conf]
  26. Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann
    Rule Extraction from Self-Organizing Networks. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:877-883 [Conf]
  27. Barbara Hammer, Marc Strickert, Thomas Villmann
    Learning Vector Quantization for Multimodal Data. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:370-376 [Conf]
  28. J. Michael Herrmann, Thomas Villmann
    Vector Quantization by Optimal Neural Gas. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:625-630 [Conf]
  29. Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Tom Fischer, Marie Cottrell
    Prototype Based Classification Using Information Theoretic Learning. [Citation Graph (0, 0)][DBLP]
    ICONIP (2), 2006, pp:40-49 [Conf]
  30. Thomas Villmann, R. Haupt, Klaus Hering
    Parallel Evolutionary Algorithms with SOM-Like Migration and its Application to VLSI-Design. [Citation Graph (0, 0)][DBLP]
    IJCNN (5), 2000, pp:167-172 [Conf]
  31. Thomas Villmann, Wieland Hermann, Michael Geyer
    Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Maps. [Citation Graph (0, 0)][DBLP]
    ISMDA, 2000, pp:138-151 [Conf]
  32. Ralf Der, Thomas Villmann
    Dynamics of Self-Organized Feature Mapping. [Citation Graph (0, 0)][DBLP]
    IWANN, 1993, pp:312-315 [Conf]
  33. 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. [Citation Graph (0, 0)][DBLP]
    NC, 1998, pp:130-136 [Conf]
  34. Klaus Hering, R. Haupt, Thomas Villmann
    Hierarchical Strategy of Model Partitioning for VLSI-Design Using an Improved Mixture of Experts Approach. [Citation Graph (0, 0)][DBLP]
    Workshop on Parallel and Distributed Simulation, 1996, pp:106-113 [Conf]
  35. Jutta Huhse, Thomas Villmann, Peter Merz, Andreas Zell
    Evolution Strategy with Neighborhood Attraction Using a Neural Gas Approach. [Citation Graph (0, 0)][DBLP]
    PPSN, 2002, pp:391-400 [Conf]
  36. Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
    Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. [Citation Graph (0, 0)][DBLP]
    WILF, 2005, pp:290-296 [Conf]
  37. Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann
    Supervised Batch Neural Gas. [Citation Graph (0, 0)][DBLP]
    ANNPR, 2006, pp:33-45 [Conf]
  38. Thomas Villmann, Udo Seiffert, Frank-Michael Schleif, Cornelia Brüß, Tina Geweniger, Barbara Hammer
    Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. [Citation Graph (0, 0)][DBLP]
    ANNPR, 2006, pp:46-56 [Conf]
  39. Jens Christian Claussen, Thomas Villmann
    Magnification control in winner relaxing neural gas. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:63, n:, pp:125-137 [Journal]
  40. Marie Cottrell, Barbara Hammer, Thomas Villmann
    New Aspects in Neurocomputing. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:63, n:, pp:1-3 [Journal]
  41. Jochen J. Steil, Gavin C. Cawley, Thomas Villmann
    Trends in Neurocomputing at ESANN 2004. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:64, n:, pp:1-4 [Journal]
  42. Marc Strickert, Udo Seiffert, Nese Sreenivasulu, Winfriede Weschke, Thomas Villmann, Barbara Hammer
    Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2006, v:69, n:7-9, pp:651-659 [Journal]
  43. Thomas Villmann
    Neural maps for faithful data modelling in medicine - state-of-the-art and exemplary applications. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2002, v:48, n:1-4, pp:229-250 [Journal]
  44. Thomas Villmann
    Special issue on new aspects in neurocomputing. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2004, v:57, n:, pp:1-2 [Journal]
  45. Thomas Villmann, Hans-Ulrich Bauer
    Applications of the growing self-organizing map. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 1998, v:21, n:1-3, pp:91-100 [Journal]
  46. Thomas Villmann, Beate Villmann, Volker Slowik
    Evolutionary algorithms with neighborhood cooperativeness according to neural maps. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2004, v:57, n:, pp:151-169 [Journal]
  47. Thomas Villmann, Jens Christian Claussen
    Magnification Control in Self-Organizing Maps and Neural Gas. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2006, v:18, n:2, pp:446-469 [Journal]
  48. Hans-Ulrich Bauer, J. Michael Herrmann, Thomas Villmann
    Neural maps and topographic vector quantization. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1999, v:12, n:4-5, pp:659-676 [Journal]
  49. Barbara Hammer, Thomas Villmann
    Generalized relevance learning vector quantization. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2002, v:15, n:8-9, pp:1059-1068 [Journal]
  50. Thomas Villmann, Erzsébet Merényi, Barbara Hammer
    Neural maps in remote sensing image analysis. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2003, v:16, n:3-4, pp:389-403 [Journal]
  51. Marie Cottrell, Barbara Hammer, Alexander Hasenfuss, Thomas Villmann
    Batch and median neural gas. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2006, v:19, n:6-7, pp:762-771 [Journal]
  52. Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
    Comparison of relevance learning vector quantization with other metric adaptive classification methods. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2006, v:19, n:5, pp:610-622 [Journal]
  53. Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Wieland Hermann
    Fuzzy classification by fuzzy labeled neural gas. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2006, v:19, n:6-7, pp:772-779 [Journal]
  54. Barbara Hammer, Marc Strickert, Thomas Villmann
    Supervised Neural Gas with General Similarity Measure. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 2005, v:21, n:1, pp:21-44 [Journal]
  55. Barbara Hammer, Marc Strickert, Thomas Villmann
    On the Generalization Ability of GRLVQ Networks. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 2005, v:21, n:2, pp:109-120 [Journal]
  56. Thomas Villmann, Frank-Michael Schleif, Erzsébet Merényi, Barbara Hammer
    Fuzzy Labeled Self-Organizing Map for Classification of Spectra. [Citation Graph (0, 0)][DBLP]
    IWANN, 2007, pp:556-563 [Conf]
  57. 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. [Citation Graph (0, 0)][DBLP]
    IWANN, 2007, pp:1036-1044 [Conf]
  58. Alexander Hasenfuss, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann
    Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes. [Citation Graph (0, 0)][DBLP]
    IWANN, 2007, pp:539-546 [Conf]
  59. Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
    Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps. [Citation Graph (0, 0)][DBLP]
    WILF, 2007, pp:563-570 [Conf]
  60. Frank-Michael Schleif, Barbara Hammer, Thomas Villmann
    Margin-based active learning for LVQ networks. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2007, v:70, n:7-9, pp:1215-1224 [Journal]
  61. Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
    Prototype-based fuzzy classification with local relevance for proteomics. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2006, v:69, n:16-18, pp:2425-2428 [Journal]
  62. Barbara Hammer, Alexander Hasenfuss, Thomas Villmann
    Magnification control for batch neural gas. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2007, v:70, n:7-9, pp:1225-1234 [Journal]

  63. Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy. [Citation Graph (, )][DBLP]


  64. 07131 Summary -- Similarity-based Clustering and its Application to Medicine and Biology. [Citation Graph (, )][DBLP]


  65. 07131 Abstracts Collection -- Similarity-based Clustering and its Application to Medicine and Biology. [Citation Graph (, )][DBLP]


  66. Some Theoretical Aspects of the Neural Gas Vector Quantizer. [Citation Graph (, )][DBLP]


  67. Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. [Citation Graph (, )][DBLP]


  68. Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. [Citation Graph (, )][DBLP]


  69. How to process uncertainty in machine learning?. [Citation Graph (, )][DBLP]


  70. Machine learning approches and pattern recognition for spectral data. [Citation Graph (, )][DBLP]


  71. Magnification Control in Relational Neural Gas. [Citation Graph (, )][DBLP]


  72. Metric adaptation for supervised attribute rating. [Citation Graph (, )][DBLP]


  73. Generalized matrix learning vector quantizer for the analysis of spectral data. [Citation Graph (, )][DBLP]


  74. Divergence Based Online Learning in Vector Quantization. [Citation Graph (, )][DBLP]


  75. Association Learning in SOMs for Fuzzy-Classification. [Citation Graph (, )][DBLP]


  76. Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data. [Citation Graph (, )][DBLP]


  77. Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. [Citation Graph (, )][DBLP]


  78. Tanimoto Metric in Tree-SOM for Improved Representation of Mass Spectrometry Data with an Underlying Taxonomic Structure. [Citation Graph (, )][DBLP]


  79. Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity. [Citation Graph (, )][DBLP]


  80. Generalized Derivative Based Kernelized Learning Vector Quantization. [Citation Graph (, )][DBLP]


  81. Intuitive Clustering of Biological Data. [Citation Graph (, )][DBLP]


  82. Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. [Citation Graph (, )][DBLP]


  83. The Mathematics of Divergence Based Online Learning in Vector Quantization. [Citation Graph (, )][DBLP]


  84. Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. [Citation Graph (, )][DBLP]


  85. Cluster Analysis of Cortical Pyramidal Neurons Using SOM. [Citation Graph (, )][DBLP]


  86. Robust Centroid-Based Clustering using Derivatives of Pearson Correlation. [Citation Graph (, )][DBLP]


  87. Functional Principal Component Learning Using Oja's Method and Sobolev Norms. [Citation Graph (, )][DBLP]


  88. Fuzzy Variant of Affinity Propagation in Comparison to Median Fuzzy c-Means. [Citation Graph (, )][DBLP]


  89. Hierarchical PCA Using Tree-SOM for the Identification of Bacteria. [Citation Graph (, )][DBLP]


  90. Cancer informatics by prototype networks in mass spectrometry. [Citation Graph (, )][DBLP]


  91. Time behavior of topological ordering in self-organizing feature mapping. [Citation Graph (, )][DBLP]


  92. Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. [Citation Graph (, )][DBLP]


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