Differentiable Kernels in Generalized Matrix Learning Vector Quantization. - Marika Kästner; David Nebel; Martin Riedel; Michael Biehl; Thomas Villmann (ICMLA (1), 2012)

ICMLA Face Recognition Challenge - Results of the Team Computational Intelligence Mittweida. - Thomas Villmann; Marika Kästner; David Nebel; Martin Riedel (ICMLA (2), 2012)

A Median Variant of Generalized Learning Vector Quantization. - David Nebel; Barbara Hammer; Thomas Villmann (ICONIP (2), 2013)

Supervised Generative Models for Learning Dissimilarity Data. - David Nebel; Barbara Hammer; Thomas Villmann (ESANN, 2014)

Non-euclidean Principal Component Analysis for Matrices by Hebbian Learning. - Mandy Lange; David Nebel; Thomas Villmann (ICAISC (1), 2014)

Rejection Strategies for Learning Vector Quantization - A Comparison of Probabilistic and Deterministic Approaches. - Lydia Fischer; David Nebel; Thomas Villmann; Barbara Hammer; Heiko Wersing (WSOM, 2014)

Generative versus Discriminative Prototype Based Classification. - Barbara Hammer; David Nebel; Martin Riedel; Thomas Villmann (WSOM, 2014)

Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization. - Mandy Lange; David Nebel; Thomas Villmann (WSOM, 2014)

Lateral enhancement in adaptive metric learning for functional data. - Thomas Villmann; Marika Kaden; David Nebel; Martin Riedel (Neurocomputing, 2014)

Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. - Thomas Villmann; Marika Kaden; David Nebel; Michael Biehl (CAIP (2), 2015)

Median-LVQ for classification of dissimilarity data based on ROC-optimization. - David Nebel; Thomas Villmann (ESANN, 2015)

Building the Library of Rna 3D Nucleotide Conformations Using the Clustering Approach. - Tomasz Zok; Maciej Antczak; Martin Riedel; David Nebel; Thomas Villmann; Piotr Lukasiak; Jacek Blazewicz; Marta Szachniuk (Int. J. Appl. Math. Comput. Sci., 2015)

Median variants of learning vector quantization for learning of dissimilarity data. - David Nebel; Barbara Hammer; Kathleen Frohberg; Thomas Villmann (Neurocomputing, 2015)

Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities. - Marika Kaden; David Nebel; Thomas Villmann (ESANN, 2016)

Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning - A Mathematical Characterization. - Thomas Villmann; Marika Kaden; David Nebel; Andrea Bohnsack (ICAISC (2), 2016)

Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning. - Sascha Saralajew; David Nebel; Thomas Villmann (ICONIP (3), 2016)

Optimization of Statistical Evaluation Measures for Classification by Median Learning Vector Quantization. - David Nebel; Thomas Villmann (WSOM, 2016)

Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities. - Marika Kaden; David Nebel; Friedrich Melchert; Andreas Backhaus; Udo Seiffert; Thomas Villmann (WSOM, 2017)

Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning. - David Nebel; Marika Kaden; Andrea Villmann; Thomas Villmann (Neurocomputing, 2017)

Investigation of Activation Functions for Generalized Learning Vector Quantization. - Thomas Villmann; Jensun Ravichandran; Andrea Villmann; David Nebel; Marika Kaden (WSOM+, 2019)

Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison. - Thomas Villmann; John Ravichandran; Andrea Villmann; David Nebel; Marika Kaden (CoRR, 2019)