Thomas Villmann - Publications

Affiliations: 
Mathematics University of Applied Sciences, Mittweida, Germany 

61 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2019 Bittrich S, Kaden M, Leberecht C, Kaiser F, Villmann T, Labudde D. Application of an interpretable classification model on Early Folding Residues during protein folding. Biodata Mining. 12: 1. PMID 30627219 DOI: 10.1186/s13040-018-0188-2  0.88
2016 Biehl M, Hammer B, Villmann T. Prototype-based models in machine learning. Wiley Interdisciplinary Reviews. Cognitive Science. 7: 92-111. PMID 26800334 DOI: 10.1002/wcs.1378  0.88
2016 Villmann T, Kaden M, Hermann W, Biehl M. Learning vector quantization classifiers for ROC-optimization Computational Statistics. 1-22. DOI: 10.1007/s00180-016-0678-y  0.88
2016 Gay M, Kaden M, Biehl M, Lampe A, Villmann T. Complex variants of GLVQ based on Wirtinger’s calculus Advances in Intelligent Systems and Computing. 428: 293-303. DOI: 10.1007/978-3-319-28518-4_26  0.88
2016 Nebel D, Villmann T. Optimization of statistical evaluation measures for classification by median learning vector quantization Advances in Intelligent Systems and Computing. 428: 281-291. DOI: 10.1007/978-3-319-28518-4_25  0.88
2016 Biehl M, Hammer B, Villmann T. Prototype-based models in machine learning Wiley Interdisciplinary Reviews: Cognitive Science. 7: 92-111. DOI: 10.1002/wcs.1378  0.88
2015 Zok T, Antczak M, Riedel M, Nebel D, Villmann T, Lukasiak P, Blazewicz J, Szachniuk M. Building the Library of RNA 3D Nucleotide Conformations Using the Clustering Approach International Journal of Applied Mathematics and Computer Science. 25: 689-700. DOI: 10.1515/amcs-2015-0050  0.88
2015 Biehl M, Hammer B, Schleif FM, Schneider P, Villmann T. Stationarity of Matrix Relevance LVQ Proceedings of the International Joint Conference On Neural Networks. 2015. DOI: 10.1109/IJCNN.2015.7280441  0.88
2015 Villmann T, Kaden M, Lange M, Sturmer P, Hermann W. Precision-recall-optimization in learning vector quantization classifiers for improved medical classification systems Ieee Ssci 2014 - 2014 Ieee Symposium Series On Computational Intelligence - Cidm 2014: 2014 Ieee Symposium On Computational Intelligence and Data Mining, Proceedings. 71-77. DOI: 10.1109/CIDM.2014.7008150  0.88
2015 Nebel D, Hammer B, Frohberg K, Villmann T. Median variants of learning vector quantization for learning of dissimilarity data Neurocomputing. 169: 295-305. DOI: 10.1016/j.neucom.2014.12.096  0.88
2015 Lange M, Biehl M, Villmann T. Non-Euclidean principal component analysis by Hebbian learning Neurocomputing. 147: 107-119. DOI: 10.1016/j.neucom.2013.11.049  0.88
2015 Villmann T, Kaden M, Nebel D, Biehl M. Learning vector quantization with adaptive cost-based outlier-rejection Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9257: 772-782. DOI: 10.1007/978-3-319-23117-4_66  0.88
2014 Kaden M, Lange M, Nebel D, Riedel M, Geweniger T, Villmann T. Aspects in classification learning - Review of recent developments in learning vector quantization Foundations of Computing and Decision Sciences. 39: 79-105. DOI: 10.2478/fcds-2014-0006  0.88
2014 Villmann T, Kaden M, Nebel D, Riedel M. Lateral enhancement in adaptive metric learning for functional data Neurocomputing. 131: 23-31. DOI: 10.1016/j.neucom.2013.07.049  0.88
2014 Kaden M, Riedel M, Hermann W, Villmann T. Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines Soft Computing. 19: 2423-2434. DOI: 10.1007/s00500-014-1496-1  0.88
2014 Biehl M, Hammer B, Villmann T. Distance measures for prototype based classification Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8603: 100-116. DOI: 10.1007/978-3-319-12084-3_9  0.88
2014 Kaden M, Hermann W, Villmann T. Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment Advances in Intelligent Systems and Computing. 295: 77-87. DOI: 10.1007/978-3-319-07695-9_7  0.88
2014 Lange M, Nebel D, Villmann T. Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization Advances in Intelligent Systems and Computing. 295: 259-269. DOI: 10.1007/978-3-319-07695-9_25  0.88
2014 Klingner M, Hellbach S, Riedel M, Kaden M, Villmann T, Böhme HJ. RFSOM - Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation Advances in Intelligent Systems and Computing. 295: 157-166. DOI: 10.1007/978-3-319-07695-9_15  0.88
2014 Hellbach S, Himstedt M, Bahrmann F, Riedel M, Villmann T, Böhme HJ. Some Room for GLVQ: Semantic Labeling of Occupancy Grid Maps Advances in Intelligent Systems and Computing. 295: 133-143. DOI: 10.1007/978-3-319-07695-9_13  0.88
2014 Hammer B, Nebel D, Riedel M, Villmann T. Generative versus Discriminative Prototype Based Classification Advances in Intelligent Systems and Computing. 295: 123-132. DOI: 10.1007/978-3-319-07695-9_12  0.88
2014 Fischer L, Nebel D, Villmann T, Hammer B, Wersing H. Rejection Strategies for Learning Vector Quantization - A Comparison of Probabilistic and Deterministic Approaches Advances in Intelligent Systems and Computing. 295: 109-118. DOI: 10.1007/978-3-319-07695-9_10  0.88
2014 Lange M, Nebel D, Villmann T. Non-euclidean principal component analysis for matrices by Hebbian learning Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8467: 77-88. DOI: 10.1007/978-3-319-07173-2_8  0.88
2013 Geweniger T, Fischer L, Kaden M, Lange M, Villmann T. Clustering by fuzzy neural gas and evaluation of fuzzy clusters. Computational Intelligence and Neuroscience. 2013: 165248. PMID 24396342 DOI: 10.1155/2013/165248  0.88
2013 Lange M, Kastner M, Villmann T. About analysis and robust classification of searchlight fMRI-data using machine learning classifiers Proceedings of the International Joint Conference On Neural Networks. DOI: 10.1109/IJCNN.2013.6706990  0.88
2013 Strickert M, Hammer B, Villmann T, Biehl M. Regularization and improved interpretation of linear data mappings and adaptive distance measures Proceedings of the 2013 Ieee Symposium On Computational Intelligence and Data Mining, Cidm 2013 - 2013 Ieee Symposium Series On Computational Intelligence, Ssci 2013. 10-17. DOI: 10.1109/CIDM.2013.6597211  0.88
2013 Nebel D, Hammer B, Villmann T. A median variant of generalized learning vector quantization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8227: 19-26. DOI: 10.1007/978-3-642-42042-9_3  0.88
2013 Kästner M, Riedel M, Strickert M, Hermann W, Villmann T. Border-sensitive learning in kernelized learning vector quantization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7902: 357-366. DOI: 10.1007/978-3-642-38679-4_35  0.88
2013 Biehl M, Kästner M, Lange M, Villmann T. Non-Euclidean principal component analysis and Oja's learning rule - Theoretical aspects Advances in Intelligent Systems and Computing. 198: 23-33. DOI: 10.1007/978-3-642-35230-0_3  0.88
2012 Bunte K, Schneider P, Hammer B, Schleif FM, Villmann T, Biehl M. Limited Rank Matrix Learning, discriminative dimension reduction and visualization Neural Networks. 26: 159-173. PMID 22041220 DOI: 10.1016/j.neunet.2011.10.001  0.88
2012 Peters G, Bunte K, Strickert M, Biehl M, Villmann T. Visualization of processes in self-learning systems 2012 10th Annual International Conference On Privacy, Security and Trust, Pst 2012. 244-249. DOI: 10.1109/PST.2012.6297953  0.88
2012 Biehl M, Bunte K, Schleif FM, Schneider P, Villmann T. Large margin linear discriminative visualization by matrix relevance learning Proceedings of the International Joint Conference On Neural Networks. DOI: 10.1109/IJCNN.2012.6252627  0.88
2012 Kastner M, Nebel D, Riedel M, Biehl M, Villmann T. Differentiable kernels in generalized matrix learning vector quantization Proceedings - 2012 11th International Conference On Machine Learning and Applications, Icmla 2012. 1: 132-137. DOI: 10.1109/ICMLA.2012.231  0.88
2012 Villmann T, Kastner M, Nebel D, Riedel M. ICMLA face recognition challenge - Results of the team computational intelligence Mittweida Proceedings - 2012 11th International Conference On Machine Learning and Applications, Icmla 2012. 2: 592-595. DOI: 10.1109/ICMLA.2012.196  0.88
2012 Bunte K, Haase S, Biehl M, Villmann T. Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences Neurocomputing. 90: 23-45. DOI: 10.1016/j.neucom.2012.02.034  0.88
2012 Kästner M, Hammer B, Biehl M, Villmann T. Functional relevance learning in generalized learning vector quantization Neurocomputing. 90: 85-95. DOI: 10.1016/j.neucom.2011.11.029  0.88
2011 Schleif FM, Villmann T, Hammer B, Schneider P. Efficient Kernelized prototype based classification. International Journal of Neural Systems. 21: 443-57. PMID 22131298 DOI: 10.1142/S012906571100295X  0.56
2011 Villmann T, Haase S. Divergence-based vector quantization. Neural Computation. 23: 1343-92. PMID 21299418 DOI: 10.1162/NECO_a_00110  0.64
2011 Bunte K, Hammer B, Villmann T, Biehl M, Wismüller A. Neighbor embedding XOM for dimension reduction and visualization Neurocomputing. 74: 1340-1350. DOI: 10.1016/j.neucom.2010.11.027  0.88
2011 Mwebaze E, Schneider P, Schleif FM, Aduwo JR, Quinn JA, Haase S, Villmann T, Biehl M. Divergence-based classification in learning vector quantization Neurocomputing. 74: 1429-1435. DOI: 10.1016/j.neucom.2010.10.016  0.88
2010 Schneider P, Bunte K, Stiekema H, Hammer B, Villmann T, Biehl M. Regularization in matrix relevance learning. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 21: 831-40. PMID 20236882 DOI: 10.1109/TNN.2010.2042729  0.88
2010 Schleif FM, Villmann T, Hammer B, Schneider P, Biehl M. Generalized derivative based kernelized learning vector quantization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6283: 21-28. DOI: 10.1007/978-3-642-15381-5_3  0.88
2010 Villmann T, Haase S, Schleif FM, Hammer B, Biehl M. The mathematics of divergence based online learning in vector quantization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5998: 108-119. DOI: 10.1007/978-3-642-12159-3_10  0.88
2009 Günther PA, Kühn HJ, Villmann T, Hermann W. Fine motor skills disorders in the course of Wilson's disease. Annals of Indian Academy of Neurology. 12: 28-34. PMID 20151006 DOI: 10.4103/0972-2327.48849  0.88
2009 Schleif FM, Villmann T, Kostrzewa M, Hammer B, Gammerman A. Cancer informatics by prototype networks in mass spectrometry. Artificial Intelligence in Medicine. 45: 215-28. PMID 18778925 DOI: 10.1016/j.artmed.2008.07.018  0.56
2009 Biehl M, Hammer B, Schneider P, Villmann T. Metric learning for prototype-based classification Studies in Computational Intelligence. 247: 183-199. DOI: 10.1007/978-3-642-04003-0_8  0.88
2009 Strickert M, Keilwagen J, Schleif FM, Villmann T, Biehl M. Matrix metric adaptation linear discriminant analysis of biomedical data Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5517: 933-940. DOI: 10.1007/978-3-642-02478-8_117  0.88
2009 Villmann T, Hammer B, Biehl M. Some theoretical aspects of the neural gas vector quantizer Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5400: 23-34. DOI: 10.1007/978-3-642-01805-3_2  0.88
2008 Villmann T, Schleif FM, Kostrzewa M, Walch A, Hammer B. Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics. 9: 129-43. PMID 18334515 DOI: 10.1093/bib/bbn009  0.56
2008 Villmann T, Hammer B, Schleif FM, Hermann W, Cottrell M. Fuzzy classification using information theoretic learning vector quantization Neurocomputing. 71: 3070-3076. DOI: 10.1016/j.neucom.2008.04.048  0.88
2008 Strickert M, Schneider P, Keilwagen J, Villmann T, Biehl M, Hammer B. Discriminatory data mapping by matrix-based supervised learning metrics Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5064: 78-89. DOI: 10.1007/978-3-540-69939-2_8  0.88
2007 Villmann T, Schleif FM, Merenyi E, Hammer B. Fuzzy labeled self-organizing map for classification of spectra Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4507: 556-563.  0.88
2006 Cottrell M, Hammer B, Hasenfuss A, Villmann T. Batch and median neural gas. Neural Networks : the Official Journal of the International Neural Network Society. 19: 762-71. PMID 16782307 DOI: 10.1016/j.neunet.2006.05.018  0.56
2006 Hammer B, Villmann T, Schleif FM, Albani C, Hermann W. Learning vector quantization classification with local relevance determination for medical data Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4029: 603-612. DOI: 10.1007/11785231_63  0.88
2005 Villmann T, Schleif F, Hammer B. Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks : the Official Journal of the International Neural Network Society. 19: 610-22. PMID 16343848 DOI: 10.1016/j.neunet.2005.07.013  0.32
2005 Steil JJ, Cawley GC, Villmann T. Trends in Neurocomputing at ESANN 2004 Neurocomputing. 64: 1-4. DOI: 10.1016/j.neucom.2004.11.020  0.88
2003 Villmann T, Merényi E, Hammer B. Neural maps in remote sensing image analysis. Neural Networks : the Official Journal of the International Neural Network Society. 16: 389-403. PMID 12672434 DOI: 10.1016/S0893-6080(03)00021-2  0.56
2002 Hammer B, Villmann T. Generalized relevance learning vector quantization. Neural Networks : the Official Journal of the International Neural Network Society. 15: 1059-68. PMID 12416694  0.56
2002 Hermann W, Eggers B, Barthel H, Clark D, Villmann T, Hesse S, Grahmann F, Kühn HJ, Sabri O, Wagner A. Correlation between automated writing movements and striatal dopaminergic innervation in patients with Wilson's disease Journal of Neurology. 249: 1082-1087. PMID 12195459 DOI: 10.1007/s00415-002-0795-0  0.88
2002 Hermann W, Caca K, Eggers B, Villmann T, Clark D, Berr F, Wagner A. Genotype correlation with fine motor symptoms in patients with Wilson's disease European Neurology. 48: 97-101. PMID 12186999 DOI: 10.1159/000062992  0.88
1997 Villmann T, Der R, Herrmann M, Martinetz TM. Topology preservation in self-organizing feature maps: exact definition and measurement. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 8: 256-66. PMID 18255630 DOI: 10.1109/72.557663  0.88
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