Dynamics of Self-Organized Feature Mapping. - Ralf Der; Thomas Villmann (IWANN, 1993)

Topology Preservation in Self-Organizing Feature Maps: General Definition and Efficient Measurement. - Thomas Villmann; Ralf Der; J. Michael Herrmann; Thomas Martinetz (Fuzzy Days, 1994)

Hierarchical Strategy of Model Partitioning for VLSI-Design Using an Improved Mixture of Experts Approach. - Klaus Hering; Reiner Haupt; Thomas Villmann (Workshop on Parallel and Distributed Simulation, 1996)

Topologieerhaltung in selbstorganisierenden neuronalen Merkmalskarten. - Thomas Villmann (Deutsch, 1996)

Measuring topology preservation in maps of real-world data. - J. Michael Herrmann; Hans-Ulrich Bauer; Thomas Villmann (ESANN, 1997)

Application of Evolutionary Algorithms to the Problem of New Clustering of Psychological Categories Using Real Clinical Data Sets. - Thomas Villmann; Beate Villmann; Conny Albani (Fuzzy Days, 1997)

Vector Quantization by Optimal Neural Gas. - J. Michael Herrmann; Thomas Villmann (ICANN, 1997)

Time behavior of topological ordering in self-organizing feature mapping. - Ralf Der; J. Michael Herrmann; Thomas Villmann (Biol. Cybern., 1997)

Growing a hypercubical output space in a self-organizing feature map. - Hans-Ulrich Bauer; Thomas Villmann (IEEE Trans. Neural Networks, 1997)

Topology preservation in self-organizing feature maps: exact definition and measurement. - Thomas Villmann; Ralf Der; J. Michael Herrmann; Thomas Martinetz (IEEE Trans. Neural Networks, 1997)

Magnification control in neural maps. - Thomas Villmann; J. Michael Herrmann (ESANN, 1998)

Evolutionary Algorithms with Self-Organizing Population Dynamic for Clustering of Categories in Psychotherapy Research Using Large Clinical Data Sets. - Thomas Villmann; A. Körner; Conny Albani (NC, 1998)

Applications of the growing self-organizing map. - Thomas Villmann; Hans-Ulrich Bauer (Neurocomputing, 1998)

Benefits and limits of the self-organizing map and its variants in the area of satellite remote sensoring processing. - Thomas Villmann (ESANN, 1999)

Parallel Evolutionary Algorithms with SOM-Like Migration and their Application to Real World Data Sets. - Thomas Villmann; Reiner Haupt; Klaus Hering; Hendrik Schulze (ICANNGA, 1999)

Neural maps and topographic vector quantization. - Hans-Ulrich Bauer; J. Michael Herrmann; Thomas Villmann (Neural Networks, 1999)

Monitoring of Physiological Parameters of Patients and Therapists During Psychotherapy Sessions Using Self-Organizing Maps. - Thomas Villmann; Bernhard Badel; Daniel Kämpf; Michael Geyer (ANNIMAB, 2000)

Neural networks approaches in medicine - a review of actual developments. - Thomas Villmann (ESANN, 2000)

Parallel Evolutionary Algorithms with SOM-Like Migration and its Application to VLSI-Design. - Thomas Villmann; Reiner Haupt; Klaus Hering (IJCNN (5), 2000)

Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Maps. - Thomas Villmann; Wieland Hermann; Michael Geyer (ISMDA, 2000)

Input pruning for neural gas architectures. - Barbara Hammer; Thomas Villmann (ESANN, 2001)

Evolutionary algorithms and neural networks in hybrid systems. - Thomas Villmann (ESANN, 2001)

Clustering of Categoric Data in Medicine - Application of Evolutionary Algorithms. - Thomas Villmann; Conny Albani (Fuzzy Days, 2001)

Estimating Relevant Input Dimensions for Self-organizing Algorithms. - Barbara Hammer; Thomas Villmann (WSOM, 2001)

Batch-RLVQ. - Barbara Hammer; Thomas Villmann (ESANN, 2002)

Exploratory Data Analysis in Medicine and Bioinformatics. - Axel Wismüller; Thomas Villmann (ESANN, 2002)

Rule Extraction from Self-Organizing Networks. - Barbara Hammer; Andreas Rechtien; Marc Strickert; Thomas Villmann (ICANN, 2002)

Learning Vector Quantization for Multimodal Data. - Barbara Hammer; Marc Strickert; Thomas Villmann (ICANN, 2002)

Evolution Strategy with Neighborhood Attraction Using a Neural Gas Approach. - Jutta Huhse; Thomas Villmann; Peter Merz; Andreas Zell (PPSN, 2002)

Evolutionary algorithms using a neural network like migration scheme. - Thomas Villmann (Integr. Comput. Aided Eng., 2002)

Neural maps for faithful data modelling in medicine - state-of-the-art and exemplary applications. - Thomas Villmann (Neurocomputing, 2002)

Generalized relevance learning vector quantization. - Barbara Hammer; Thomas Villmann (Neural Networks, 2002)

Mathematical Aspects of Neural Networks. - Barbara Hammer; Thomas Villmann (ESANN, 2003)

Neural maps in remote sensing image analysis. - Thomas Villmann; Erzsébet Merényi; Barbara Hammer (Neural Networks, 2003)

Theory and applications of neural maps. - Thomas Villmann; Udo Seiffert; Axel Wismüller (ESANN, 2004)

Relevance LVQ versus SVM. - Barbara Hammer; Marc Strickert; Thomas Villmann (ICAISC, 2004)

Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data. - Frank-Michael Schleif; U. Clauss; Thomas Villmann; Barbara Hammer (ICMLA, 2004)

Special issue on new aspects in neurocomputing. - Thomas Villmann (Neurocomputing, 2004)

Evolutionary algorithms with neighborhood cooperativeness according to neural maps. - Thomas Villmann; Beate Villmann; Volker Slowik (Neurocomputing, 2004)

Relevance learning for mental disease classification. - Barbara Hammer; Andreas Rechtien; Marc Strickert; Thomas Villmann (ESANN, 2005)

Classification using non-standard metrics. - Barbara Hammer; Thomas Villmann (ESANN, 2005)

Generalized Relevance LVQ with Correlation Measures for Biological Data. - Marc Strickert; Nese Sreenivasulu; Winfriede Weschke; Udo Seiffert; Thomas Villmann (ESANN, 2005)

Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. - Thomas Villmann; Frank-Michael Schleif; Barbara Hammer (ICMLA, 2005)

Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. - Frank-Michael Schleif; Thomas Villmann; Barbara Hammer (WILF, 2005)

Magnification control in winner relaxing neural gas. - Jens Christian Claussen; Thomas Villmann (Neurocomputing, 2005)

New Aspects in Neurocomputing. - Marie Cottrell; Barbara Hammer; Thomas Villmann (Neurocomputing, 2005)

Trends in Neurocomputing at ESANN 2004. - Jochen J. Steil; Gavin C. Cawley; Thomas Villmann (Neurocomputing, 2005)

Supervised Neural Gas with General Similarity Measure. - Barbara Hammer; Marc Strickert; Thomas Villmann (Neural Process. Lett., 2005)

On the Generalization Ability of GRLVQ Networks. - Barbara Hammer; Marc Strickert; Thomas Villmann (Neural Process. Lett., 2005)

Supervised Batch Neural Gas. - Barbara Hammer; Alexander Hasenfuss; Frank-Michael Schleif; Thomas Villmann (ANNPR, 2006)

Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. - Thomas Villmann; Udo Seiffert; Frank-Michael Schleif; Cornelia Brüß; Tina Geweniger; Barbara Hammer (ANNPR, 2006)

Perspectives of Self-adapted Self-organizing Clustering in Organic Computing. - Thomas Villmann; Barbara Hammer; Udo Seiffert (BioADIT, 2006)

Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps. - Frank-Michael Schleif; Thomas Elssner; Markus Kostrzewa; Thomas Villmann; Barbara Hammer (CBMS, 2006)

Fuzzy image segmentation with Fuzzy Labelled Neural Gas. - Cornelia Brüß; Felix Bollenbeck; Frank-Michael Schleif; Winfriede Weschke; Thomas Villmann; Udo Seiffert (ESANN, 2006)

Neural networks and machine learning in bioinformatics - theory and applications. - Udo Seiffert; Barbara Hammer; Samuel Kaski; Thomas Villmann (ESANN, 2006)

Learning Vector Quantization Classification with Local Relevance Determination for Medical Data. - Barbara Hammer; Thomas Villmann; Frank-Michael Schleif; Cornelia Albani; Wieland Hermann (ICAISC, 2006)

Prototype Based Classification Using Information Theoretic Learning. - Thomas Villmann; Barbara Hammer; Frank-Michael Schleif; Tina Geweniger; Tom Fischer; Marie Cottrell (ICONIP (2), 2006)

Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis. - Marc Strickert; Udo Seiffert; Nese Sreenivasulu; Winfriede Weschke; Thomas Villmann; Barbara Hammer (Neurocomputing, 2006)

Prototype-based fuzzy classification with local relevance for proteomics. - Thomas Villmann; Frank-Michael Schleif; Barbara Hammer (Neurocomputing, 2006)

Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern. - Barbara Hammer; Thomas Villmann (Künstliche Intell., 2006)

Magnification Control in Self-Organizing Maps and Neural Gas. - Thomas Villmann; Jens Christian Claussen (Neural Comput., 2006)

Batch and median neural gas. - Marie Cottrell; Barbara Hammer; Alexander Hasenfuss; Thomas Villmann (Neural Networks, 2006)

Fuzzy classification by fuzzy labeled neural gas. - Thomas Villmann; Barbara Hammer; Frank-Michael Schleif; Tina Geweniger; Wieland Hermann (Neural Networks, 2006)

Comparison of relevance learning vector quantization with other metric adaptive classification methods. - Thomas Villmann; Frank-Michael Schleif; Barbara Hammer (Neural Networks, 2006)

07131 Summary – Similarity-based Clustering and its Application to Medicine and Biology. - Michael Biehl; Barbara Hammer; Michel Verleysen; Thomas Villmann (Similarity-based Clustering and its Application to Medicine and Biology, 2007)

07131 Abstracts Collection – Similarity-based Clustering and its Application to Medicine and Biology. - Michael Biehl; Barbara Hammer; Michel Verleysen; Thomas Villmann (Similarity-based Clustering and its Application to Medicine and Biology, 2007)

How to process uncertainty in machine learning?. - Barbara Hammer; Thomas Villmann (ESANN, 2007)

Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. - Thomas Villmann; Marc Strickert; Cornelia Brüß; Frank-Michael Schleif; Udo Seiffert (ESANN, 2007)

Association Learning in SOMs for Fuzzy-Classification. - Thomas Villmann; Frank-Michael Schleif; Martijn van der Werff; André M. Deelder; Rob A. E. M. Tollenaar (ICMLA, 2007)

Intuitive Clustering of Biological Data. - Barbara Hammer; Alexander Hasenfuss; Frank-Michael Schleif; Thomas Villmann; Marc Strickert; Udo Seiffert (IJCNN, 2007)

Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes. - Alexander Hasenfuss; Barbara Hammer; Frank-Michael Schleif; Thomas Villmann (IWANN, 2007)

Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra. - Frank-Michael Schleif; Thomas Villmann; Barbara Hammer (IWANN, 2007)

Fuzzy Labeled Self-Organizing Map for Classification of Spectra. - Thomas Villmann; Frank-Michael Schleif; Erzsébet Merényi; Barbara Hammer (IWANN, 2007)

Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps. - Frank-Michael Schleif; Thomas Villmann; Barbara Hammer (WILF, 2007)

Magnification control for batch neural gas. - Barbara Hammer; Alexander Hasenfuss; Thomas Villmann (Neurocomputing, 2007)

Margin-based active learning for LVQ networks. - Frank-Michael Schleif; Barbara Hammer; Thomas Villmann (Neurocomputing, 2007)

Explicit Magnification Control of Self-Organizing Maps for "Forbidden" Data. - Erzsébet Merényi; Abha Jain; Thomas Villmann (IEEE Trans. Neural Networks, 2007)

Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. - Marc Strickert; Petra Schneider; Jens Keilwagen; Thomas Villmann; Michael Biehl; Barbara Hammer (ANNPR, 2008)

Robust Centroid-Based Clustering using Derivatives of Pearson Correlation. - Marc Strickert; Nese Sreenivasulu; Thomas Villmann; Barbara Hammer (BIOSIGNALS (2), 2008)

Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy. - Frank-Michael Schleif; Matthias Ongyerth; Thomas Villmann (CBMS, 2008)

Magnification Control in Relational Neural Gas. - Alexander Hasenfuss; Barbara Hammer; Tina Geweniger; Thomas Villmann (ESANN, 2008)

Generalized matrix learning vector quantizer for the analysis of spectral data. - Petra Schneider; Frank-Michael Schleif; Thomas Villmann; Michael Biehl (ESANN, 2008)

Metric adaptation for supervised attribute rating. - Marc Strickert; Frank-Michael Schleif; Thomas Villmann (ESANN, 2008)

Machine learning approches and pattern recognition for spectral data. - Thomas Villmann; Erzsébet Merényi; Udo Seiffert (ESANN, 2008)

Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity. - Tina Geweniger; Frank-Michael Schleif; Alexander Hasenfuss; Barbara Hammer; Thomas Villmann (ICONIP (2), 2008)

Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. - Marc Strickert; Frank-Michael Schleif; Udo Seiffert; Thomas Villmann (Inteligencia Artif., 2008)

Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. - Thomas Villmann; Frank-Michael Schleif; Markus Kostrzewa; Axel Walch; Barbara Hammer (Briefings Bioinform., 2008)

Prototype based fuzzy classification in clinical proteomics. - Frank-Michael Schleif; Thomas Villmann; Barbara Hammer (Int. J. Approx. Reason., 2008)

Fuzzy classification using information theoretic learning vector quantization. - Thomas Villmann; Barbara Hammer; Frank-Michael Schleif; Wieland Hermann; Marie Cottrell (Neurocomputing, 2008)

Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers. - Frank-Michael Schleif; Thomas Villmann; Barbara Hammer; Martijn van der Werff; André M. Deelder; Rob A. E. M. Tollenaar (Computational Intelligence in Biomedicine and Bioinformatics, 2008)

09081 Abstracts Collection - Similarity-based learning on structures. - Michael Biehl; Barbara Hammer; Sepp Hochreiter; Stefan C. Kremer; Thomas Villmann (Similarity-based learning on structures, 2009)

09081 Summary - Similarity-based learning on structures. - Michael Biehl; Barbara Hammer; Sepp Hochreiter; Stefan C. Kremer; Thomas Villmann (Similarity-based learning on structures, 2009)

Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. - Marc Strickert; Frank-Michael Schleif; Thomas Villmann; Udo Seiffert (Similarity-Based Clustering, 2009)

Some Theoretical Aspects of the Neural Gas Vector Quantizer. - Thomas Villmann; Barbara Hammer; Michael Biehl (Similarity-Based Clustering, 2009)

Median Variant of Fuzzy c-Means. - Tina Geweniger; Dietlind Zühlke; Barbara Hammer; Thomas Villmann (ESANN, 2009)

Neural Maps and Learning Vector Quantization - Theory and Applications. - Frank-Michael Schleif; Thomas Villmann (ESANN, 2009)

Fuzzy Fleiss-kappa for Comparison of Fuzzy Classifiers. - Dietlind Zühlke; Tina Geweniger; Ulrich Heimann; Thomas Villmann (ESANN, 2009)

Tanimoto Metric in Tree-SOM for Improved Representation of Mass Spectrometry Data with an Underlying Taxonomic Structure. - Stephan Simmuteit; Frank-Michael Schleif; Thomas Villmann; Thomas Elssner (ICMLA, 2009)

Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. - Marc Strickert; Jens Keilwagen; Frank-Michael Schleif; Thomas Villmann; Michael Biehl (IWANN (1), 2009)

Funtional vector quantization by neural maps. - Thomas Villmann; Frank-Michael Schleif (WHISPERS, 2009)

Fuzzy Variant of Affinity Propagation in Comparison to Median Fuzzy c-Means. - Tina Geweniger; Dietlind Zühlke; Barbara Hammer; Thomas Villmann (WSOM, 2009)

Hierarchical PCA Using Tree-SOM for the Identification of Bacteria. - Stephan Simmuteit; Frank-Michael Schleif; Thomas Villmann; Markus Kostrzewa (WSOM, 2009)

Functional Principal Component Learning Using Oja's Method and Sobolev Norms. - Thomas Villmann; Barbara Hammer (WSOM, 2009)

Cancer informatics by prototype networks in mass spectrometry. - Frank-Michael Schleif; Thomas Villmann; Markus Kostrzewa; Barbara Hammer; Alexander Gammerman (Artif. Intell. Medicine, 2009)

Supervised data analysis and reliability estimation with exemplary application for spectral data. - Frank-Michael Schleif; Thomas Villmann; Matthias Ongyerth (Neurocomputing, 2009)

Prototype Based Classification in Bioinformatics. - Frank-Michael Schleif; Thomas Villmann; Barbara Hammer (Encyclopedia of Artificial Intelligence, 2009)

Metric Learning for Prototype-Based Classification. - Michael Biehl; Barbara Hammer; Petra Schneider; Thomas Villmann (Innovations in Neural Information Paradigms and Applications, 2009)

Cluster Analysis of Cortical Pyramidal Neurons Using SOM. - Andreas Schierwagen; Thomas Villmann; Alán Alpár; Ulrich Gärtner (ANNPR, 2010)

The Mathematics of Divergence Based Online Learning in Vector Quantization. - Thomas Villmann; Sven Haase; Frank-Michael Schleif; Barbara Hammer; Michael Biehl (ANNPR, 2010)

Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. - Kerstin Bunte; Barbara Hammer; Thomas Villmann; Michael Biehl; Axel Wismüller (ESANN, 2010)

Extending FSNPC to handle data points with fuzzy class assignments. - Tina Geweniger; Thomas Villmann (ESANN, 2010)

Divergence based Learning Vector Quantization. - Ernest Mwebaze; Petra Schneider; Frank-Michael Schleif; Sven Haase; Thomas Villmann; Michael Biehl (ESANN, 2010)

Sparse representation of data. - Thomas Villmann; Frank-Michael Schleif; Barbara Hammer (ESANN, 2010)

Learning vector quantization for heterogeneous structured data. - Dietlind Zühlke; Frank-Michael Schleif; Tina Geweniger; Sven Haase; Thomas Villmann (ESANN, 2010)

Divergence Based Online Learning in Vector Quantization. - Thomas Villmann; Sven Haase; Frank-Michael Schleif; Barbara Hammer (ICAISC (1), 2010)

Generalized Derivative Based Kernelized Learning Vector Quantization. - Frank-Michael Schleif; Thomas Villmann; Barbara Hammer; Petra Schneider; Michael Biehl (IDEAL, 2010)

Divergence based vector quantization of spectral data. - Thomas Villmann; Sven Haase (WHISPERS, 2010)

Median fuzzy c-means for clustering dissimilarity data. - Tina Geweniger; Dietlind Zühlke; Barbara Hammer; Thomas Villmann (Neurocomputing, 2010)

Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. - Stephan Simmuteit; Frank-Michael Schleif; Thomas Villmann; Barbara Hammer (Knowl. Inf. Syst., 2010)

Regularization in matrix relevance learning. - Petra Schneider; Kerstin Bunte; Han Stiekema; Barbara Hammer; Thomas Villmann; Michael Biehl (IEEE Trans. Neural Networks, 2010)

Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. - Kerstin Bunte; Frank-Michael Schleif; Sven Haase; Thomas Villmann (ESANN, 2011)

Optimization of Parametrized Divergences in Fuzzy c-Means. - Tina Geweniger; Marika Kästner; Thomas Villmann (ESANN, 2011)

Generalized functional relevance learning vector quantization. - Marika Kästner; Barbara Hammer; Michael Biehl; Thomas Villmann (ESANN, 2011)

Multivariate class labeling in Robust Soft LVQ. - Petra Schneider; Tina Geweniger; Frank-Michael Schleif; Michael Biehl; Thomas Villmann (ESANN, 2011)

Multispectral image characterization by partial generalized covariance. - Marc Strickert; Björn Labitzke; Andreas Kolb 001; Thomas Villmann (ESANN, 2011)

Information theory related learning. - Thomas Villmann; José C. Príncipe; Andrzej Cichocki (ESANN, 2011)

Magnification in divergence based neural maps. - Thomas Villmann; Sven Haase (IJCNN, 2011)

Functional relevance learning in learning vector quantization for hyperspectral data. - M. Kästner; Thomas Villmann (WHISPERS, 2011)

Relevance Learning in Unsupervised Vector Quantization Based on Divergences. - Marika Kästner; Andreas Backhaus; Tina Geweniger; Sven Haase; Udo Seiffert; Thomas Villmann (WSOM, 2011)

Sparse Functional Relevance Learning in Generalized Learning Vector Quantization. - Thomas Villmann; Marika Kästner (WSOM, 2011)

Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). - Michael Biehl; Barbara Hammer; Erzsébet Merényi; Alessandro Sperduti; Thomas Villmann (Dagstuhl Reports, 2011)

Efficient Kernelized Prototype Based Classification. - Frank-Michael Schleif; Thomas Villmann; Barbara Hammer; Petra Schneider (Int. J. Neural Syst., 2011)

Neighbor embedding XOM for dimension reduction and visualization. - Kerstin Bunte; Barbara Hammer; Thomas Villmann; Michael Biehl; Axel Wismüller (Neurocomputing, 2011)

Divergence-based classification in learning vector quantization. - Ernest Mwebaze; Petra Schneider; Frank-Michael Schleif; Jennifer R. Aduwo; John A. Quinn; Sven Haase; Thomas Villmann; Michael Biehl (Neurocomputing, 2011)

Divergence-Based Vector Quantization. - Thomas Villmann; Sven Haase (Neural Comput., 2011)

Recent developments in clustering algorithms. - Charles Bouveyron; Barbara Hammer; Thomas Villmann (ESANN, 2012)

Modified Conn-Index for the evaluation of fuzzy clusterings. - Tina Geweniger; Marika Kästner; Mandy Lange; Thomas Villmann (ESANN, 2012)

Integration of Structural Expert Knowledge about Classes for Classification Using the Fuzzy Supervised Neural Gas. - Marika Kästner; Wieland Hermann; Thomas Villmann (ESANN, 2012)

Unmixing Hyperspectral Images with Fuzzy Supervised Self-Organizing Maps. - Thomas Villmann; Erzsébet Merényi; William H. Farrand (ESANN, 2012)

Fuzzy Supervised Self-Organizing Map for Semi-supervised Vector Quantization. - Marika Kästner; Thomas Villmann (ICAISC (1), 2012)

Fuzzy Neural Gas for Unsupervised Vector Quantization. - Thomas Villmann; Tina Geweniger; Marika Kästner; Mandy Lange (ICAISC (1), 2012)

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)

Large margin linear discriminative visualization by Matrix Relevance Learning. - Michael Biehl; Kerstin Bunte; Frank-Michael Schleif; Petra Schneider; Thomas Villmann (IJCNN, 2012)

Visualization of processes in self-learning systems. - Gabriele Peters; Kerstin Bunte; Marc Strickert; Michael Biehl; Thomas Villmann (PST, 2012)

Non-Euclidean Principal Component Analysis and Oja's Learning Rule - Theoretical Aspects. - Michael Biehl; Marika Kästner; Mandy Lange; Thomas Villmann (WSOM, 2012)

Gradient Based Learning in Vector Quantization Using Differentiable Kernels. - Thomas Villmann; Sven Haase; Marika Kästner (WSOM, 2012)

Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. - Kerstin Bunte; Sven Haase; Michael Biehl; Thomas Villmann (Neurocomputing, 2012)

Functional relevance learning in generalized learning vector quantization. - Marika Kästner; Barbara Hammer; Michael Biehl; Thomas Villmann (Neurocomputing, 2012)

Limited Rank Matrix Learning, discriminative dimension reduction and visualization. - Kerstin Bunte; Petra Schneider; Barbara Hammer; Frank-Michael Schleif; Thomas Villmann; Michael Biehl (Neural Networks, 2012)

Distance Measures for Prototype Based Classification. - Michael Biehl; Barbara Hammer; Thomas Villmann (BrainComp, 2013)

Regularization and improved interpretation of linear data mappings and adaptive distance measures. - Marc Strickert; Barbara Hammer; Thomas Villmann; Michael Biehl (CIDM, 2013)

Border sensitive fuzzy vector quantization in semi-supervised learning. - Tina Geweniger; Marika Kästner; Thomas Villmann (ESANN, 2013)

A sparse kernelized matrix learning vector quantization model for human activity recognition. - Marika Kästner; Marc Strickert; Thomas Villmann (ESANN, 2013)

Non-Euclidean independent component analysis and Oja's learning. - Mandy Lange; Michael Biehl; Thomas Villmann (ESANN, 2013)

Regularization in relevance learning vector quantization using l1-norms. - Martin Riedel; Fabrice Rossi; Marika Kästner; Thomas Villmann (ESANN, 2013)

Processing Hyperspectral Data in Machine Learning. - Thomas Villmann; Marika Kästner; Andreas Backhaus; Udo Seiffert (ESANN, 2013)

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

About analysis and robust classification of searchlight fMRI-data using machine learning classifiers. - Mandy Lange; Marika Kästner; Thomas Villmann (IJCNN, 2013)

Border-Sensitive Learning in Kernelized Learning Vector Quantization. - Marika Kästner; Martin Riedel; Marc Strickert; Wieland Hermann; Thomas Villmann (IWANN (1), 2013)

Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters. - Tina Geweniger; Lydia Fischer; Marika Kaden; Mandy Lange; Thomas Villmann (Comput. Intell. Neurosci., 2013)

Regularization in Relevance Learning Vector Quantization Using l one Norms. - Martin Riedel; Marika Kästner; Fabrice Rossi; Thomas Villmann (CoRR, 2013)

Editorial A Successful Change From TNN to TNNLS and a Very Successful Year. - Derong Liu 001; Charles Anderson 001; Ahmad Taher Azar; Giorgio Battistelli; Eduardo Bayro-Corrochano; Cristiano Cervellera; David A. Elizondo; Maurizio Filippone; Giorgio Gnecco; Xiaolin Hu; Tingwen Huang; Weifeng Liu 001; et al. (IEEE Trans. Neural Networks Learn. Syst., 2013)

Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems. - Thomas Villmann; Marika Kaden; Mandy Lange; Paul Sturmer; Wieland Hermann (CIDM, 2014)

Utilization of Chemical Structure Information for Analysis of Spectra Composites. - Kristin Domaschke; André Roßberg; Thomas Villmann (ESANN, 2014)

Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization. - Marika Kaden; Wieland Hermann; Thomas Villmann (ESANN, 2014)

Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization. - Mandy Lange; Dietlind Zühlke; Olaf Holz; Thomas Villmann (ESANN, 2014)

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

Recent trends in learning of structured and non-standard data. - Frank-Michael Schleif; Peter Tiño; Thomas Villmann (ESANN, 2014)

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

High Dimensional Matrix Relevance Learning. - Frank-Michael Schleif; Thomas Villmann; Xibin Zhu (ICDM Workshops, 2014)

Find Rooms for Improvement: Towards Semi-automatic Labeling of Occupancy Grid Maps. - Sven Hellbach; Marian Himstedt; Frank Bahrmann; Martin Riedel; Thomas Villmann; Hans-Joachim Böhme (ICONIP (3), 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)

Probabilistic Prototype Classification Using t-norms. - Tina Geweniger; Frank-Michael Schleif; Thomas Villmann (WSOM, 2014)

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

Some Room for GLVQ: Semantic Labeling of Occupancy Grid Maps. - Sven Hellbach; Marian Himstedt; Frank Bahrmann; Martin Riedel; Thomas Villmann; Hans-Joachim Böhme (WSOM, 2014)

Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment. - Marika Kaden; Wieland Hermann; Thomas Villmann (WSOM, 2014)

RFSOM - Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation. - Mathias Klingner; Sven Hellbach; Martin Riedel; Marika Kaden; Thomas Villmann; Hans-Joachim Böhme (WSOM, 2014)

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

Special issue on new challenges in neural computation 2012. - Barbara Hammer; Thomas Villmann (Neurocomputing, 2014)

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

Sophisticated LVQ Classification Models - Beyond Accuracy Optimization. - Thomas Villmann (BrainComp, 2015)

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

Learning matrix quantization and variants of relevance learning. - Kristin Domaschke; Marika Kaden; Mandy Lange; Thomas Villmann (ESANN, 2015)

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

Mathematical Characterization of Sophisticated Variants for Relevance Learning in Learning Matrix Quantization Based on Schatten-p-norms. - Andrea Bohnsack; Kristin Domaschke; Marika Kaden; Mandy Lange; Thomas Villmann (ICAISC (1), 2015)

Stationarity of Matrix Relevance LVQ. - Michael Biehl; Barbara Hammer; Frank-Michael Schleif; Petra Schneider; Thomas Villmann (IJCNN, 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)

Non-Euclidean principal component analysis by Hebbian learning. - Mandy Lange; Michael Biehl; Thomas Villmann (Neurocomputing, 2015)

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

Kernelized vector quantization in gradient-descent learning. - Thomas Villmann; Sven Haase; Marika Kaden (Neurocomputing, 2015)

Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines. - Marika Kaden; Martin Riedel; Wieland Hermann; Thomas Villmann (Soft Comput., 2015)

Probabilistic Modeling in Machine Learning. - Davide Bacciu; Paulo J. G. Lisboa; Alessandro Sperduti; Thomas Villmann (Handbook of Computational Intelligence, 2015)

Prototype-based Models for the Supervised Learning of Classification Schemes. - Michael Biehl; Barbara Hammer; Thomas Villmann (Astroinformatics, 2016)

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)

Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning. - Sascha Saralajew; Thomas Villmann (IJCNN, 2016)

Complex Variants of GLVQ Based on Wirtinger's Calculus. - Matthias Gay; Marika Kaden; Michael Biehl; Alexander Lampe; Thomas Villmann (WSOM, 2016)

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

Self-Adjusting Reject Options in Prototype Based Classification. - Thomas Villmann; Marika Kaden; Andrea Bohnsack; J.-M. Villmann; T. Drogies; Sascha Saralajew; Barbara Hammer (WSOM, 2016)

Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261). - Gyan Bhanot; Michael Biehl; Thomas Villmann; Dietlind Zühlke (Dagstuhl Reports, 2016)

Learning matrix quantization and relevance learning based on Schatten-p-norms. - Andrea Bohnsack; Kristin Domaschke; Marika Kaden; Mandy Lange; Thomas Villmann (Neurocomputing, 2016)

Biomedical data analysis in translational research: integration of expert knowledge and interpretable models. - Gyan Bhanot; Michael Biehl; Thomas Villmann; Dietlind Zühlke (ESANN, 2017)

Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. - Mohammad Mohammadi; Michael Biehl; Andrea Villmann; Thomas Villmann (ICAISC (1), 2017)

Transfer learning in classification based on manifolc. models and its relation to tangent metric learning. - Sascha Saralajew; Thomas Villmann (IJCNN, 2017)

Relational and median variants of Possibilistic Fuzzy C-Means. - Tina Geweniger; Thomas Villmann (WSOM, 2017)

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)

Prototypes and matrix relevance learning in complex fourier space. - Michiel Straat; Marika Kaden; Matthias Gay; Thomas Villmann; Alexander Lampe; Udo Seiffert; Michael Biehl; Friedrich Melchert (WSOM, 2017)

Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. - Thomas Villmann; Michael Biehl; Andrea Villmann; Sascha Saralajew (WSOM, 2017)

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

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning. - Thomas Villmann; Andrea Bohnsack; Marika Kaden (J. Artif. Intell. Soft Comput. Res., 2017)

Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. - Andrea Villmann; Marika Kaden; Sascha Saralajew; Wieland Hermann; Thomas Villmann (ESANN, 2018)

Direct Incorporation of L_1 -Regularization into Generalized Matrix Learning Vector Quantization. - Falko Lischke; Thomas Neumann 007; Sven Hellbach; Thomas Villmann; Hans-Joachim Böhme (ICAISC (1), 2018)

Multi-class and Cluster Evaluation Measures Based on Rényi and Tsallis Entropies and Mutual Information. - Thomas Villmann; Tina Geweniger (ICAISC (1), 2018)

Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. - Andrea Villmann; Marika Kaden; Sascha Saralajew; Thomas Villmann (ICAISC (1), 2018)

Learning Vector Quantization Methods for Interpretable Classification Learning and Multilayer Networks. - Thomas Villmann (IJCCI, 2018)

Prototype-based Neural Network Layers: Incorporating Vector Quantization. - Sascha Saralajew; Lars Holdijk; Maike Rees; Thomas Villmann (CoRR, 2018)

Learning vector quantization classifiers for ROC-optimization. - Thomas Villmann; Marika Kaden; Wieland Hermann; Michael Biehl (Comput. Stat., 2018)

Statistical physics of learning and inference. - Michael Biehl; Nestor Caticha; Manfred Opper; Thomas Villmann (ESANN, 2019)

DropConnect for Evaluation of Classification Stability in Learning Vector Quantization. - Jensun Ravichandran; Sascha Saralajew; Thomas Villmann (ESANN, 2019)

Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis. - Thomas Villmann; Marika Kaden; Mehrdad Mohannazadeh Bakhtiari; Andrea Villmann (ICAISC (2), 2019)

Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components. - Sascha Saralajew; Lars Holdijk; Maike Rees; Ebubekir Asan; Thomas Villmann (NeurIPS, 2019)

Variants of Fuzzy Neural Gas. - Tina Geweniger; Thomas Villmann (WSOM+, 2019)

Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks. - Sascha Saralajew; Lars Holdijk; Maike Rees; Thomas Villmann (WSOM+, 2019)

Searching for the Origins of Life - Detecting RNA Life Signatures Using Learning Vector Quantization. - Thomas Villmann; Marika Kaden; Szymon Wasik; Mateusz Kudla; Kaja Gutowska; Andrea Villmann; Jacek Blazewicz (WSOM+, 2019)

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

Application of an interpretable classification model on Early Folding Residues during protein folding. - Sebastian Bittrich; Marika Kaden; Christoph Leberecht; Florian Kaiser; Thomas Villmann; Dirk Labudde (BioData Min., 2019)

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

Quantum-Inspired Learning Vector Quantization for Classification Learning. - Thomas Villmann; Jensun Ravichandran; Alexander Engelsberger; Andrea Villmann; Marika Kaden (ESANN, 2020)

A Mathematical Model for Optimum Error-Reject Trade-Off for Learning of Secure Classification Models in the Presence of Label Noise During Training. - Seyedfakhredin Musavishavazi; Mehrdad Mohannazadeh Bakhtiari; Thomas Villmann (ICAISC (1), 2020)

Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms. - Sascha Saralajew; Lars Holdijk; Thomas Villmann (NeurIPS, 2020)

Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability. - Jensun Ravichandran; Marika Kaden; Sascha Saralajew; Thomas Villmann (Neurocomputing, 2020)

Learning vector quantization and relevances in complex coefficient space. - Michiel Straat; Marika Kaden; Matthias Gay; Thomas Villmann; Alexander Lampe; Udo Seiffert; Michael Biehl; Friedrich Melchert (Neural Comput. Appl., 2020)

The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach. - Marika Kaden; Ronny Schubert; Mehrdad Mohannazadeh Bakhtiari; Lucas Schwarz; Thomas Villmann (ESANN, 2021)

The Coming of Age of Interpretable and Explainable Machine Learning Models. - Paulo Lisboa; Sascha Saralajew; Alfredo Vellido; Thomas Villmann (ESANN, 2021)

RecLVQ: Recurrent Learning Vector Quantization. - Jensun Ravichandran; Thomas Villmann; Marika Kaden (ESANN, 2021)

Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks. - Seyedfakhredin Musavishavazi; Marika Kaden; Thomas Villmann (ICAISC (1), 2021)

Quantum-Hybrid Neural Vector Quantization - A Mathematical Approach. - Thomas Villmann; Alexander Engelsberger (ICAISC (1), 2021)

Sensors data fusion for smart decisions making: A novel bi-functional system for the evaluation of sensors contribution in classification problems. - Feryel Zoghlami; Marika Kaden; Thomas Villmann; Germar Schneider; Harald Heinrich (ICIT, 2021)

ToF/Radar early feature-based fusion system for human detection and tracking. - Feryel Zoghlami; Okan Kamil Sen; Harald Heinrich; Germar Schneider; Emec Ercelik; Alois C. Knoll; Thomas Villmann (ICIT, 2021)

Virxicon: a lexicon of viral sequences. - Mateusz Kudla; Kaja Gutowska; Jaroslaw Synak; Mirko Weber; Katrin Sophie Bohnsack; Piotr Lukasiak; Thomas Villmann; Jacek Blazewicz; Marta Szachniuk (Bioinform., 2021)

Brilliant Challenges Optimization Problem Submission Contest Final Report. - Jan Badura; Artur Laskowski; Maciej Antczak; Jacek Blazewicz; Grzegorz Pawlak; Erwin Pesch; Thomas Villmann; Szymon Wasik (CoRR, 2021)

The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers. - Katrin Sophie Bohnsack; Marika Kaden; Julia Abel; Sascha Saralajew; Thomas Villmann (Entropy, 2021)

AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task. - Feryel Zoghlami; Marika Kaden; Thomas Villmann; Germar Schneider; Harald Heinrich (Sensors, 2021)

Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features. - Katrin Sophie Bohnsack; Marika Kaden; Julius Voigt; Thomas Villmann (ESANN, 2022)

Tutorial - Machine Learning and Information Theoretic Methods for Molecular Biology and Medicine. - Thomas Villmann; Jonas S. Almeida; John A. Lee; Susana Vinga (ESANN, 2022)

Multilayer Perceptrons with Banach-Like Perceptrons Based on Semi-inner Products - About Approximation Completeness. - Thomas Villmann; Alexander Engelsberger (ICAISC (1), 2022)

Classification by Components Including Chow's Reject Option. - Mehrdad Mohannazadeh Bakhtiari; Thomas Villmann (ICONIP (4), 2022)

Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models. - Danny Möbius; Jensun Ravichandran; Marika Kaden; Thomas Villmann (ICONIP (2), 2022)

A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. - Thomas Villmann; Daniel Staps; Jensun Ravichandran; Sascha Saralajew; Michael Biehl; Marika Kaden (IDA, 2022)

Prototype-based One-Class-Classification Learning Using Local Representations. - Daniel Staps; Ronny Schubert; Marika Kaden; Alexander Lampe; Wieland Hermann; Thomas Villmann (IJCNN, 2022)

Variants of recurrent learning vector quantization. - Jensun Ravichandran; Marika Kaden; Thomas Villmann (Neurocomputing, 2022)

Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences. - Marika Kaden; Katrin Sophie Bohnsack; Mirko Weber; Mateusz Kudla; Kaja Gutowska; Jacek Blazewicz; Thomas Villmann (Neural Comput. Appl., 2022)

Quantum-inspired learning vector quantizers for prototype-based classification. - Thomas Villmann; Alexander Engelsberger; Jensun Ravichandran; Andrea Villmann; Marika Kaden (Neural Comput. Appl., 2022)

Efficient Representation of Biochemical Structures for Supervised and Unsupervised Machine Learning Models Using Multi-Sensoric Embeddings. - Katrin Sophie Bohnsack; Alexander Engelsberger; Marika Kaden; Thomas Villmann (BIOINFORMATICS, 2023)

Learning Vector Quantization in Context of Information Bottleneck Theory. - Mehrdad Mohannazadeh Bakhtiari; Daniel Staps; Thomas Villmann (ESANN, 2023)

Quantum-ready vector quantization: Prototype learning as a binary optimization problem. - Alexander Engelsberger; Thomas Villmann (ESANN, 2023)

Quantum Artificial Intelligence: A tutorial. - José D. Martín-Guerrero; Lucas Lamata; Thomas Villmann (ESANN, 2023)

Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection. - Maximilian Münch; Katrin Sophie Bohnsack; Alexander Engelsberger; Frank-Michael Schleif; Thomas Villmann (ESANN, 2023)

Variants of Neural Gas for Regression Learning. - Thomas Villmann; Ronny Schubert; Marika Kaden (ESANN, 2023)

An Interpretable Two-Layered Neural Network Structure-Based on Component-Wise Reasoning. - Mehrdad Mohannazadeh Bakhtiari; Thomas Villmann (ICAISC (1), 2023)

The Geometry of Decision Borders Between Affine Space Prototypes for Nearest Prototype Classifiers. - Mehrdad Mohannazadeh Bakhtiari; Andrea Villmann; Thomas Villmann (ICAISC (1), 2023)

Reducing Computer Vision Dataset Size via Selective Sampling. - Hanno Stage; Lukas Ewecker; Jacob Langner; Tin Stribor Sohn; Thomas Villmann; Eric Sax (ITSC, 2023)

A White-Box Workflow for the Prediction of Food Content From Near-Infrared Data Based on Fourier-Transformation. - Ronny Schubert; Lynn V. Reuss; Daniel Staps; Marika Kaden; Thomas Villmann; Robert Hasler; Robin Herz; Till Tiemann; Wolfram Richardt (WHISPERS, 2023)

Compression of Particle Images for Inspection of Microgravity Experiments by Means of a Symmetric Structural Auto-Encoder. - Daniel Staps; Marika Kaden; Jan Auth; Florian Zaussinger; Thomas Villmann (WHISPERS, 2023)

The coming of age of interpretable and explainable machine learning models. - Paulo J. G. Lisboa; Sascha Saralajew; Alfredo Vellido; Ricardo Fernández-Domenech; Thomas Villmann (Neurocomputing, 2023)

Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective. - Katrin Sophie Bohnsack; Marika Kaden; Julia Abel; Thomas Villmann (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

Quantum Computing Approaches for Vector Quantization - Current Perspectives and Developments. - Alexander Engelsberger; Thomas Villmann (Entropy, 2023)

Multi-proximity based embedding scheme for learning vector quantization-based classification of biochemical structured data. - Katrin Sophie Bohnsack; Julius Voigt; Marika Kaden; Florian Heinke; Thomas Villmann (Neurocomputing, 2023)

Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge. - Julius Voigt; Sascha Saralajew; Marika Kaden; Katrin Sophie Bohnsack; Lynn V. Reuss; Thomas Villmann (BIOSTEC (1), 2024)

Domain Knowledge Integration in Machine Learning Systems - An Introduction. - Marika Kaden; Sascha Saralajew; Thomas Villmann (ESANN, 2024)

About Vector Quantization and its Privacy in Federated Learning. - Ronny Schubert; Thomas Villmann (ESANN, 2024)

PVDN-Urban - A Dataset for Provident Vehicle Detection at Night in Urban Scenarios. - Lukas Ewecker; Florian Schiffel; Robin Schwager; Tim Brühl; Tin Stribor Sohn; Thomas Villmann (ICIP, 2024)

Are Gamers Good Annotators? A Comparative Study of Gaming Crowds and Professional Annotators. - Lukas Ewecker; Christopher Klugmann; Daniel Kondermann; Robin Schwager; Thomas Villmann (ITSC, 2024)

Knowledge Integration in Vector Quantization Models and Corresponding Structured Covariance Estimation. - Marika Kaden; Julius Voigt; Katrin Sophie Bohnsack; Mandy Lange-Geisler; Thomas Villmann (WSOM+, 2024)

About Interpretable Learning Rules for Vector Quantizers - A Methodological Approach. - Ronny Schubert; Thomas Villmann (WSOM+, 2024)

K Minimum Enclosing Balls for Outlier Detection. - Daniel Staps; Thomas Villmann; Benjamin Paaßen (WSOM+, 2024)

Hyperbox-GLVQ Based on Min-Max-Neurons. - Thomas Villmann; T. Davies; Alexander Engelsberger (WSOM+, 2024)

Subspace corrected relevance learning with application in neuroimaging. - Rick van Veen; Neha Rajendra Bari Tamboli; Sofie Lövdal; Sanne K. Meles; Remco J. Renken; Gert-Jan de Vries; Dario Arnaldi; Silvia Morbelli; Pedro Clavero; José A. Obeso; Maria C. Rodriguez-Oroz; Klaus Leonard Leenders; et al. (Artif. Intell. Medicine, 2024)

Aligning Generalisation Between Humans and Machines. - Filip Ilievski; Barbara Hammer; Frank van Harmelen; Benjamin Paassen; Sascha Saralajew; Ute Schmid; Michael Biehl; Marianna Bolognesi; Xin Luna Dong; Kiril Gashteovski; Pascal Hitzler; Giuseppe Marra; et al. (CoRR, 2024)

A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations. - Sascha Saralajew; Ashish Rana; Thomas Villmann; Ammar Shaker (AAAI, 2025)