| 2026 | ESANN | Diminishing Returns - Data Integer Quantization and its Effects on Training Dynamics of Distance Based Classifiers. | Thomas Davies, Alexander Engelsberger, Magdalena Psenickova, Thomas Villmann |
| 2026 | ESANN | Evaluation of Rashomon Sets for the Determination of Stable and Plausible Model Explanations. | Marika Kaden, Mahrokh Karimi, Subhashree Panda, Thomas Pfaff, Thomas Villmann |
| 2026 | ESANN | Reliable Counterfactuals for Machine Learning Models - Current Aspects and Perspectives. | Marika Kaden, Benjamin Paassen, Barbara Hammer, Ronny Schubert, Thomas Villmann |
| 2026 | ESANN | Geometric-analytical Generation of Counterfactuals for Prototype-based Classifiers. | Marika Kaden, Lynn V. Reuss, Thomas Villmann |
| 2026 | ESANN | Enforcing Feature Sparseness for Reliable Classification by Prototype-Based Models. | Marika Kaden, Julius Voigt, Sascha Saralajew, Thomas Villmann |
| 2026 | ESANN | Domination Reliability Analysis Based on Graph Features Using Generalized Matrix LVQ. | Mandy Lange-Geisler, Klaus Dohmen, Thomas Villmann |
| 2026 | ESANN | Topology-Preserving Prototype Learning on Riemannian Manifolds. | Lucas Schwarz, Magdalena Psenickova, Thomas Villmann, Florian Rhrbein |
| 2025 | AAAI | A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations. | Sascha Saralajew, Ashish Rana, Thomas Villmann, Ammar Shaker |
| 2025 | ESANN | Towards Learning Vector Quantization in the Setting of Homomorphic Encryption. | Thomas Davies, Ronny Schubert, Mandy Lange-Geisler, Klaus Dohmen, Thomas Villmann |
| 2025 | ESANN | Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach - FA(IR)$^2$MA-GLVQ. | Marika Kaden, Alexander Engelsberger, Ronny Schubert, Sofie Lvdal, Elina L. van den Brandhof, Michael Biehl, Thomas Villmann |
| 2025 | ESANN | Integrating Class Relation Knowledge in Probabilistic Learning Vector Quantization. | Marika Kaden, Ronny Schubert, Tina Geweniger, Wieland Hermann, Thomas Villmann |
| 2025 | ESANN | Learning of Probability Estimates for System and Network Reliability Analysis by Means of Matrix Learning Vector Quantization. | Mandy Lange-Geisler, Klaus Dohmen, Thomas Villmann |
| 2025 | ICAISC | Prototype Learning for Classification on Spherical Manifolds and Its Relation to Quantum Classification Approaches. | Alexander Engelsberger, Magdalena Psenickova, Thomas Villmann |
| 2025 | ICAISC | Reliable Classification Learning for Medical Data Analysis Using Prototype-Based Models. | Julius Voigt, Marika Kaden, Lynn V. Reuss, Thomas Villmann |
| 2024 | ESANN | Domain Knowledge Integration in Machine Learning Systems - An Introduction. | Marika Kaden, Sascha Saralajew, Thomas Villmann |
| 2024 | ESANN | About Vector Quantization and its Privacy in Federated Learning. | Ronny Schubert, Thomas Villmann |
| 2024 | ICIP | PVDN-Urban - A Dataset for Provident Vehicle Detection at Night in Urban Scenarios. | Lukas Ewecker, Florian Schiffel, Robin Schwager, Tim Brhl, Tin Stribor Sohn, Thomas Villmann |
| 2023 | ESANN | Learning Vector Quantization in Context of Information Bottleneck Theory. | Mehrdad Mohannazadeh Bakhtiari, Daniel Staps, Thomas Villmann |
| 2023 | ESANN | Quantum-ready vector quantization: Prototype learning as a binary optimization problem. | Alexander Engelsberger, Thomas Villmann |
| 2023 | ESANN | Quantum Artificial Intelligence: A tutorial. | Jos D. Martn-Guerrero, Lucas Lamata, Thomas Villmann |
| 2023 | ESANN | Sparse Nystrm Approximation for Non-Vectorial Data Using Class-informed Landmark Selection. | Maximilian Mnch, Katrin Sophie Bohnsack, Alexander Engelsberger, Frank-Michael Schleif, Thomas Villmann |
| 2023 | ESANN | Variants of Neural Gas for Regression Learning. | Thomas Villmann, Ronny Schubert, Marika Kaden |
| 2023 | ICAISC | An Interpretable Two-Layered Neural Network Structure-Based on Component-Wise Reasoning. | Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann |
| 2023 | ICAISC | The Geometry of Decision Borders Between Affine Space Prototypes for Nearest Prototype Classifiers. | Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann, Thomas Villmann |
| 2022 | ESANN | 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 |
| 2022 | ESANN | Tutorial - Machine Learning and Information Theoretic Methods for Molecular Biology and Medicine. | Thomas Villmann, Jonas S. Almeida, John A. Lee, Susana Vinga |
| 2022 | ICAISC | Multilayer Perceptrons with Banach-Like Perceptrons Based on Semi-inner Products - About Approximation Completeness. | Thomas Villmann, Alexander Engelsberger |
| 2022 | ICONIP | Classification by Components Including Chow's Reject Option. | Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann |
| 2022 | ICONIP | Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models. | Danny Mbius, Jensun Ravichandran, Marika Kaden, Thomas Villmann |
| 2022 | IJCNN | Prototype-based One-Class-Classification Learning Using Local Representations. | Daniel Staps, Ronny Schubert, Marika Kaden, Alexander Lampe, Wieland Hermann, Thomas Villmann |
| 2022 | IDA | 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 |
| 2021 | ESANN | The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach. | Marika Kaden, Ronny Schubert, Mehrdad Mohannazadeh Bakhtiari, Lucas Schwarz, Thomas Villmann |
| 2021 | ESANN | The Coming of Age of Interpretable and Explainable Machine Learning Models. | Paulo Lisboa, Sascha Saralajew, Alfredo Vellido, Thomas Villmann |
| 2021 | ESANN | RecLVQ: Recurrent Learning Vector Quantization. | Jensun Ravichandran, Thomas Villmann, Marika Kaden |
| 2021 | ICAISC | Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks. | Seyedfakhredin Musavishavazi, Marika Kaden, Thomas Villmann |
| 2021 | ICAISC | Quantum-Hybrid Neural Vector Quantization - A Mathematical Approach. | Thomas Villmann, Alexander Engelsberger |
| 2020 | ESANN | Quantum-Inspired Learning Vector Quantization for Classification Learning. | Thomas Villmann, Jensun Ravichandran, Alexander Engelsberger, Andrea Villmann, Marika Kaden |
| 2020 | ICAISC | 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 |
| 2019 | ESANN | Statistical physics of learning and inference. | Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann |
| 2019 | ESANN | DropConnect for Evaluation of Classification Stability in Learning Vector Quantization. | Jensun Ravichandran, Sascha Saralajew, Thomas Villmann |
| 2019 | ICAISC | Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis. | Thomas Villmann, Marika Kaden, Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann |
| 2018 | ESANN | Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. | Andrea Villmann, Marika Kaden, Sascha Saralajew, Wieland Hermann, Thomas Villmann |
| 2018 | ICAISC | Direct Incorporation of L_1 -Regularization into Generalized Matrix Learning Vector Quantization. | Falko Lischke, Thomas Neumann, Sven Hellbach, Thomas Villmann, Hans-Joachim Bhme |
| 2018 | ICAISC | Multi-class and Cluster Evaluation Measures Based on Rnyi and Tsallis Entropies and Mutual Information. | Thomas Villmann, Tina Geweniger |
| 2018 | ICAISC | Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. | Andrea Villmann, Marika Kaden, Sascha Saralajew, Thomas Villmann |
| 2018 | IJCCI | Learning Vector Quantization Methods for Interpretable Classification Learning and Multilayer Networks. | Thomas Villmann |
| 2017 | ESANN | Biomedical data analysis in translational research: integration of expert knowledge and interpretable models. | Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zhlke |
| 2017 | ICAISC | Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. | Mohammad Mohammadi, Michael Biehl, Andrea Villmann, Thomas Villmann |
| 2017 | IJCNN | Transfer learning in classification based on manifolc. models and its relation to tangent metric learning. | Sascha Saralajew, Thomas Villmann |
| 2016 | ESANN | Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities. | Marika Kaden, David Nebel, Thomas Villmann |
| 2016 | ICAISC | 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 |
| 2016 | ICONIP | Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning. | Sascha Saralajew, David Nebel, Thomas Villmann |
| 2016 | IJCNN | Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning. | Sascha Saralajew, Thomas Villmann |
| 2015 | CAIP | Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. | Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl |
| 2015 | ESANN | Learning matrix quantization and variants of relevance learning. | Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann |
| 2015 | ESANN | Median-LVQ for classification of dissimilarity data based on ROC-optimization. | David Nebel, Thomas Villmann |
| 2015 | ICAISC | 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 |
| 2015 | IJCNN | Stationarity of Matrix Relevance LVQ. | Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann |
| 2014 | CIDM | Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems. | Thomas Villmann, Marika Kaden, Mandy Lange, Paul Sturmer, Wieland Hermann |
| 2014 | ESANN | Utilization of Chemical Structure Information for Analysis of Spectra Composites. | Kristin Domaschke, Andr Roberg, Thomas Villmann |
| 2014 | ESANN | Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization. | Marika Kaden, Wieland Hermann, Thomas Villmann |
| 2014 | ESANN | Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization. | Mandy Lange, Dietlind Zhlke, Olaf Holz, Thomas Villmann |
| 2014 | ESANN | Supervised Generative Models for Learning Dissimilarity Data. | David Nebel, Barbara Hammer, Thomas Villmann |
| 2014 | ESANN | Recent trends in learning of structured and non-standard data. | Frank-Michael Schleif, Peter Tio, Thomas Villmann |
| 2014 | ICAISC | Non-euclidean Principal Component Analysis for Matrices by Hebbian Learning. | Mandy Lange, David Nebel, Thomas Villmann |
| 2014 | ICDM | High Dimensional Matrix Relevance Learning. | Frank-Michael Schleif, Thomas Villmann, Xibin Zhu |
| 2014 | ICONIP | Find Rooms for Improvement: Towards Semi-automatic Labeling of Occupancy Grid Maps. | Sven Hellbach, Marian Himstedt, Frank Bahrmann, Martin Riedel, Thomas Villmann, Hans-Joachim Bhme |
| 2013 | CIDM | Regularization and improved interpretation of linear data mappings and adaptive distance measures. | Marc Strickert, Barbara Hammer, Thomas Villmann, Michael Biehl |
| 2013 | ESANN | Border sensitive fuzzy vector quantization in semi-supervised learning. | Tina Geweniger, Marika Kstner, Thomas Villmann |
| 2013 | ESANN | A sparse kernelized matrix learning vector quantization model for human activity recognition. | Marika Kstner, Marc Strickert, Thomas Villmann |
| 2013 | ESANN | Non-Euclidean independent component analysis and Oja's learning. | Mandy Lange, Michael Biehl, Thomas Villmann |
| 2013 | ESANN | Regularization in relevance learning vector quantization using l1-norms. | Martin Riedel, Fabrice Rossi, Marika Kstner, Thomas Villmann |
| 2013 | ESANN | Processing Hyperspectral Data in Machine Learning. | Thomas Villmann, Marika Kstner, Andreas Backhaus, Udo Seiffert |
| 2013 | ICONIP | A Median Variant of Generalized Learning Vector Quantization. | David Nebel, Barbara Hammer, Thomas Villmann |
| 2013 | IJCNN | About analysis and robust classification of searchlight fMRI-data using machine learning classifiers. | Mandy Lange, Marika Kstner, Thomas Villmann |
| 2013 | IWANN | Border-Sensitive Learning in Kernelized Learning Vector Quantization. | Marika Kstner, Martin Riedel, Marc Strickert, Wieland Hermann, Thomas Villmann |
| 2012 | ESANN | Recent developments in clustering algorithms. | Charles Bouveyron, Barbara Hammer, Thomas Villmann |
| 2012 | ESANN | Modified Conn-Index for the evaluation of fuzzy clusterings. | Tina Geweniger, Marika Kstner, Mandy Lange, Thomas Villmann |
| 2012 | ESANN | Integration of Structural Expert Knowledge about Classes for Classification Using the Fuzzy Supervised Neural Gas. | Marika Kstner, Wieland Hermann, Thomas Villmann |
| 2012 | ESANN | Unmixing Hyperspectral Images with Fuzzy Supervised Self-Organizing Maps. | Thomas Villmann, Erzsbet Mernyi, William H. Farrand |
| 2012 | ICAISC | Fuzzy Supervised Self-Organizing Map for Semi-supervised Vector Quantization. | Marika Kstner, Thomas Villmann |
| 2012 | ICAISC | Fuzzy Neural Gas for Unsupervised Vector Quantization. | Thomas Villmann, Tina Geweniger, Marika Kstner, Mandy Lange |
| 2012 | ICMLA | Differentiable Kernels in Generalized Matrix Learning Vector Quantization. | Marika Kstner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann |
| 2012 | ICMLA | ICMLA Face Recognition Challenge - Results of the Team Computational Intelligence Mittweida. | Thomas Villmann, Marika Kstner, David Nebel, Martin Riedel |
| 2012 | IJCNN | Large margin linear discriminative visualization by Matrix Relevance Learning. | Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann |
| 2012 | PST | Visualization of processes in self-learning systems. | Gabriele Peters, Kerstin Bunte, Marc Strickert, Michael Biehl, Thomas Villmann |
| 2011 | ESANN | Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. | Kerstin Bunte, Frank-Michael Schleif, Sven Haase, Thomas Villmann |
| 2011 | ESANN | Optimization of Parametrized Divergences in Fuzzy c-Means. | Tina Geweniger, Marika Kstner, Thomas Villmann |
| 2011 | ESANN | Generalized functional relevance learning vector quantization. | Marika Kstner, Barbara Hammer, Michael Biehl, Thomas Villmann |
| 2011 | ESANN | Multivariate class labeling in Robust Soft LVQ. | Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann |
| 2011 | ESANN | Multispectral image characterization by partial generalized covariance. | Marc Strickert, Bjrn Labitzke, Andreas Kolb, Thomas Villmann |
| 2011 | ESANN | Information theory related learning. | Thomas Villmann, Jos C. Prncipe, Andrzej Cichocki |
| 2011 | IJCNN | Magnification in divergence based neural maps. | Thomas Villmann, Sven Haase |
| 2010 | ESANN | Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. | Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismller |
| 2010 | ESANN | Extending FSNPC to handle data points with fuzzy class assignments. | Tina Geweniger, Thomas Villmann |
| 2010 | ESANN | Divergence based Learning Vector Quantization. | Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl |
| 2010 | ESANN | Sparse representation of data. | Thomas Villmann, Frank-Michael Schleif, Barbara Hammer |
| 2010 | ESANN | Learning vector quantization for heterogeneous structured data. | Dietlind Zhlke, Frank-Michael Schleif, Tina Geweniger, Sven Haase, Thomas Villmann |
| 2010 | ICAISC | Divergence Based Online Learning in Vector Quantization. | Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer |
| 2010 | IDEAL | Generalized Derivative Based Kernelized Learning Vector Quantization. | Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl |
| 2009 | ESANN | Median Variant of Fuzzy c-Means. | Tina Geweniger, Dietlind Zhlke, Barbara Hammer, Thomas Villmann |
| 2009 | ESANN | Neural Maps and Learning Vector Quantization - Theory and Applications. | Frank-Michael Schleif, Thomas Villmann |
| 2009 | ESANN | Fuzzy Fleiss-kappa for Comparison of Fuzzy Classifiers. | Dietlind Zhlke, Tina Geweniger, Ulrich Heimann, Thomas Villmann |
| 2009 | ICMLA | 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 |
| 2009 | IWANN | Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. | Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl |
| 2008 | CBMS | Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy. | Frank-Michael Schleif, Matthias Ongyerth, Thomas Villmann |
| 2008 | ESANN | Magnification Control in Relational Neural Gas. | Alexander Hasenfuss, Barbara Hammer, Tina Geweniger, Thomas Villmann |
| 2008 | ESANN | Generalized matrix learning vector quantizer for the analysis of spectral data. | Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl |
| 2008 | ESANN | Metric adaptation for supervised attribute rating. | Marc Strickert, Frank-Michael Schleif, Thomas Villmann |
| 2008 | ESANN | Machine learning approches and pattern recognition for spectral data. | Thomas Villmann, Erzsbet Mernyi, Udo Seiffert |
| 2008 | ICONIP | Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity. | Tina Geweniger, Frank-Michael Schleif, Alexander Hasenfuss, Barbara Hammer, Thomas Villmann |
| 2007 | ESANN | How to process uncertainty in machine learning?. | Barbara Hammer, Thomas Villmann |
| 2007 | ESANN | Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. | Thomas Villmann, Marc Strickert, Cornelia Br, Frank-Michael Schleif, Udo Seiffert |
| 2007 | ICMLA | Association Learning in SOMs for Fuzzy-Classification. | Thomas Villmann, Frank-Michael Schleif, Martijn van der Werff, Andr M. Deelder, Rob A. E. M. Tollenaar |
| 2007 | IJCNN | Intuitive Clustering of Biological Data. | Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann, Marc Strickert, Udo Seiffert |
| 2007 | IWANN | Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes. | Alexander Hasenfuss, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann |
| 2007 | IWANN | 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 |
| 2007 | IWANN | Fuzzy Labeled Self-Organizing Map for Classification of Spectra. | Thomas Villmann, Frank-Michael Schleif, Erzsbet Mernyi, Barbara Hammer |
| 2006 | CBMS | Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps. | Frank-Michael Schleif, Thomas Elssner, Markus Kostrzewa, Thomas Villmann, Barbara Hammer |
| 2006 | ESANN | Fuzzy image segmentation with Fuzzy Labelled Neural Gas. | Cornelia Br, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert |
| 2006 | ESANN | Magnification control for batch neural gas. | Barbara Hammer, Alexander Hasenfuss, Thomas Villmann |
| 2006 | ESANN | Margin based Active Learning for LVQ Networks. | Frank-Michael Schleif, Barbara Hammer, Thomas Villmann |
| 2006 | ESANN | Neural networks and machine learning in bioinformatics - theory and applications. | Udo Seiffert, Barbara Hammer, Samuel Kaski, Thomas Villmann |
| 2006 | ICAISC | Learning Vector Quantization Classification with Local Relevance Determination for Medical Data. | Barbara Hammer, Thomas Villmann, Frank-Michael Schleif, Cornelia Albani, Wieland Hermann |
| 2006 | ICONIP | Prototype Based Classification Using Information Theoretic Learning. | Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Tom Fischer, Marie Cottrell |
| 2005 | ESANN | Relevance learning for mental disease classification. | Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann |
| 2005 | ESANN | Classification using non-standard metrics. | Barbara Hammer, Thomas Villmann |
| 2005 | ESANN | Generalized Relevance LVQ with Correlation Measures for Biological Data. | Marc Strickert, Nese Sreenivasulu, Winfriede Weschke, Udo Seiffert, Thomas Villmann |
| 2005 | ICMLA | Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. | Thomas Villmann, Frank-Michael Schleif, Barbara Hammer |
| 2004 | ESANN | Theory and applications of neural maps. | Thomas Villmann, Udo Seiffert, Axel Wismller |
| 2004 | ICAISC | Relevance LVQ versus SVM. | Barbara Hammer, Marc Strickert, Thomas Villmann |
| 2004 | ICMLA | Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data. | Frank-Michael Schleif, U. Clauss, Thomas Villmann, Barbara Hammer |
| 2003 | ESANN | Magnification Control in Winner Relaxing Neural Gas. | Jens Christian Claussen, Thomas Villmann |
| 2003 | ESANN | Mathematical Aspects of Neural Networks. | Barbara Hammer, Thomas Villmann |
| 2002 | ESANN | Batch-RLVQ. | Barbara Hammer, Thomas Villmann |
| 2002 | ESANN | Exploratory Data Analysis in Medicine and Bioinformatics. | Axel Wismller, Thomas Villmann |
| 2002 | ICANN | Rule Extraction from Self-Organizing Networks. | Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann |
| 2002 | ICANN | Learning Vector Quantization for Multimodal Data. | Barbara Hammer, Marc Strickert, Thomas Villmann |
| 2002 | PPSN | Evolution Strategy with Neighborhood Attraction Using a Neural Gas Approach. | Jutta Huhse, Thomas Villmann, Peter Merz, Andreas Zell |
| 2001 | ESANN | Input pruning for neural gas architectures. | Barbara Hammer, Thomas Villmann |
| 2001 | ESANN | Evolutionary algorithms and neural networks in hybrid systems. | Thomas Villmann |
| 2000 | ESANN | Neural networks approaches in medicine - a review of actual developments. | Thomas Villmann |
| 2000 | IJCNN | Parallel Evolutionary Algorithms with SOM-Like Migration and its Application to VLSI-Design. | Thomas Villmann, Reiner Haupt, Klaus Hering |
| 1999 | ESANN | Benefits and limits of the self-organizing map and its variants in the area of satellite remote sensoring processing. | Thomas Villmann |
| 1998 | ESANN | Magnification control in neural maps. | Thomas Villmann, J. Michael Herrmann |
| 1997 | ESANN | Measuring topology preservation in maps of real-world data. | J. Michael Herrmann, Hans-Ulrich Bauer, Thomas Villmann |
| 1997 | ICANN | Vector Quantization by Optimal Neural Gas. | J. Michael Herrmann, Thomas Villmann |
| 1996 | PADS | Hierarchical Strategy of Model Partitioning for VLSI-Design Using an Improved Mixture of Experts Approach. | Klaus Hering, Reiner Haupt, Thomas Villmann |
| 1993 | IWANN | Dynamics of Self-Organized Feature Mapping. | Ralf Der, Thomas Villmann |