Thomas Villmann

Affiliations: 
Mathematics University of Applied Sciences, Mittweida, Germany 
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Bittrich S, Kaden M, Leberecht C, et al. (2019) Application of an interpretable classification model on Early Folding Residues during protein folding. Biodata Mining. 12: 1
Biehl M, Hammer B, Villmann T. (2016) Prototype-based models in machine learning. Wiley Interdisciplinary Reviews. Cognitive Science. 7: 92-111
Villmann T, Kaden M, Hermann W, et al. (2016) Learning vector quantization classifiers for ROC-optimization Computational Statistics. 1-22
Gay M, Kaden M, Biehl M, et al. (2016) Complex variants of GLVQ based on Wirtinger’s calculus Advances in Intelligent Systems and Computing. 428: 293-303
Nebel D, Villmann T. (2016) Optimization of statistical evaluation measures for classification by median learning vector quantization Advances in Intelligent Systems and Computing. 428: 281-291
Biehl M, Hammer B, Villmann T. (2016) Prototype-based models in machine learning Wiley Interdisciplinary Reviews: Cognitive Science. 7: 92-111
Zok T, Antczak M, Riedel M, et al. (2015) Building the Library of RNA 3D Nucleotide Conformations Using the Clustering Approach International Journal of Applied Mathematics and Computer Science. 25: 689-700
Biehl M, Hammer B, Schleif FM, et al. (2015) Stationarity of Matrix Relevance LVQ Proceedings of the International Joint Conference On Neural Networks. 2015
Villmann T, Kaden M, Lange M, et al. (2015) 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
Nebel D, Hammer B, Frohberg K, et al. (2015) Median variants of learning vector quantization for learning of dissimilarity data Neurocomputing. 169: 295-305
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