T. Villmann

Thomas Villmann, Michael Biehl, Andrea Villmann, 2017, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

Clustering of Categoric Data in Medicine - Application of Evolutionary Algorithms

Thomas Villmann, Conny Albani, T. Villmann, 2001, Fuzzy Days.

Application of Evolutionary Algorithms to the Problem of New Clustering of Psychological Categories Using Real Clinical Data Sets

Thomas Villmann, Conny Albani, Beate Villmann, 1997, Fuzzy Days.

Evolutionary algorithms using a neural network like migration scheme

Thomas Villmann, T. Villmann, 2002, Integr. Comput. Aided Eng..

Parallel Evolutionary Algorithms with SOM-Like Migration and their Application to Real World Data Sets

T. Villmann, K. Hering, R. Haupt, 1999, ICANNGA.

Kernelized vector quantization in gradient-descent learning

Thomas Villmann, Sven Haase, Marika Kaden, 2015, Neurocomputing.

Lateral enhancement in adaptive metric learning for functional data

Thomas Villmann, Martin Riedel, David Nebel, 2014, Neurocomputing.

Divergence-Based Vector Quantization

Thomas Villmann, Sven Haase, T. Villmann, 2011, Neural Computation.

Probabilistic Modeling in Machine Learning

Paulo J. G. Lisboa, Thomas Villmann, Davide Bacciu, 2015, Handbook of Computational Intelligence.

Divergence-based classification in learning vector quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2011, Neurocomputing.

Funtional vector quantization by neural maps

Thomas Villmann, Frank-Michael Schleif, T. Villmann, 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization

Thomas Villmann, Frank-Michael Schleif, Sven Haase, 2011, ESANN.

Discriminative visualization by limited rank matrix learning

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2008 .

Fine motor skills disorders in the course of Wilson's disease

T. Villmann, H. Kühn, W. Hermann, 2009, Annals of Indian Academy of Neurology.

Comparison of clinical types of Wilson's disease and glucose metabolism in extrapyramidal motor brain regions

T. Villmann, T. Villmann, S. Hesse, 2002, Journal of Neurology.

Instability and discontinuous change in the experience of therapeutic interaction: An extended single-case study of psychodynamic therapy processes

T. Villmann, M. Geyer, E. Brähler, 2010, Psychotherapy research : journal of the Society for Psychotherapy Research.

ToF/Radar early feature-based fusion system for human detection and tracking

Thomas Villmann, Germar Schneider, Harald Heinrich, 2021, 2021 22nd IEEE International Conference on Industrial Technology (ICIT).

Generalized relevance learning vector quantization

Thomas Villmann, Barbara Hammer, T. Villmann, 2002, Neural Networks.

Rejection Strategies for Learning Vector Quantization - A Comparison of Probabilistic and Deterministic Approaches

Thomas Villmann, Heiko Wersing, Barbara Hammer, 2014, WSOM.

Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2007, Briefings Bioinform..

Prototype-based Neural Network Layers: Incorporating Vector Quantization pdf

Thomas Villmann, Sascha Saralajew, Lars Holdijk, 2018, ArXiv.

The Mathematics of Divergence Based Online Learning in Vector Quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2010, ANNPR.

Learning matrix quantization and relevance learning based on Schatten-p-norms

T. Villmann, M. Lange, A. Bohnsack, 2016, Neurocomputing.

Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2007, WILF.

Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2007, IWANN.

Regression Neural Gas: Extension of Standard Neural Gas and its application for function approximation.

T. Villmann, M. Kaden, R. Schubert, 2023 .

RecLVQ: Recurrent Learning Vector Quantization

Thomas Villmann, Marika Kaden, Jensun Ravichandran, 2021, ESANN.

Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components

Thomas Villmann, Sascha Saralajew, Ebubekir Asan, 2019, NeurIPS.

Batch and median neural gas

Thomas Villmann, Barbara Hammer, Alexander Hasenfuss, 2006, Neural Networks.

Multi-proximity based embedding scheme for learning vector quantization-based classification of biochemical structured data

Katrin Sophie Bohnsack, T. Villmann, M. Kaden, 2023, Neurocomputing.

AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task

Thomas Villmann, Germar Schneider, Marika Kaden, 2021, Sensors.

Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning

Thomas Villmann, Andrea Villmann, David Nebel, 2017, Neurocomputing.

Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning

Thomas Villmann, Sascha Saralajew, T. Villmann, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Efficient Representation of Biochemical Structures for Supervised and Unsupervised Machine Learning Models Using Multi-Sensoric Embeddings

Katrin Sophie Bohnsack, T. Villmann, M. Kaden, 2023, BIOINFORMATICS.

Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features

Katrin Sophie Bohnsack, T. Villmann, M. Kaden, 2022, ESANN.

Thomas Villmann, David Nebel, Marika Kaden, 2016, ESANN.

Fuzzy Labeled Neural GAS for Fuzzy Classification

Frank-Michael Schleif, Barbara Hammer, T. Villmann, 2005 .

Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems

Thomas Villmann, Wieland Hermann, Marika Kaden, 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

Quantum-Hybrid Neural Vector Quantization - A Mathematical Approach

Thomas Villmann, Alexander Engelsberger, T. Villmann, 2021, ICAISC.

Quantum-Inspired Learning Vector Quantization for Classification Learning

Thomas Villmann, Andrea Villmann, Marika Kaden, 2020, ESANN.

Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

Thomas Villmann, Martin Riedel, Tina Geweniger, 2014 .

An Improved Mixture of Experts Approach for Model Partitioning in VLSI{Design Using Genetic Algorithms

K. Hering, R. Haupt, Th. Villmann, 1998 .

Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014

Thomas Villmann, Frank-Michael Schleif, Marika Kaden, 2014, WSOM.

Evolutionary algorithms with neighborhood cooperativeness according to neural maps

Thomas Villmann, Volker Slowik, Beate Villmann, 2004, Neurocomputing.

Statistical physics of learning and inference

Thomas Villmann, Michael Biehl, Manfred Opper, 2019, ESANN.

Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization

Thomas Villmann, David Nebel, Mandy Lange, 2014, WSOM.

Recent developments in clustering algorithms

Thomas Villmann, Charles Bouveyron, Barbara Hammer, 2012, ESANN.

About analysis and robust classification of searchlight fMRI-data using machine learning classifiers

Thomas Villmann, Marika Kaden, Mandy Lange, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

ICMLA Face Recognition Challenge -- Results of the Team Computational Intelligence Mittweida

Thomas Villmann, Martin Riedel, David Nebel, 2012, 2012 11th International Conference on Machine Learning and Applications.

Biomedical data analysis in translational research: integration of expert knowledge and interpretable models

Michael Biehl, G. Bhanot, T. Villmann, 2017, ESANN.

A sparse kernelized matrix learning vector quantization model for human activity recognition

Thomas Villmann, Marc Strickert, Marika Kaden, 2013, ESANN.

Relational and median variants of Possibilistic Fuzzy C-Means

Thomas Villmann, Tina Geweniger, T. Villmann, 2017, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach

Thomas Villmann, Andrea Villmann, Wieland Hermann, 2018, ESANN.

Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences

Michael Biehl, T. Villmann, S. Haase, 2012, Neurocomputing.

Functional relevance learning in generalized learning vector quantization

Michael Biehl, T. Villmann, B. Hammer, 2012, Neurocomputing.

Large margin linear discriminative visualization by Matrix Relevance Learning

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines

Thomas Villmann, Martin Riedel, Wieland Hermann, 2015, Soft Comput..

Association Learning in SOMs for Fuzzy-Classification

Thomas Villmann, Frank-Michael Schleif, Martijn van der Werff, 2007, Sixth International Conference on Machine Learning and Applications (ICMLA 2007).

Adaptive Matrix Metrics for Attribute Dependence Analysis in Differential High-Throughput Data

Michael Biehl, J. Keilwagen, T. Villmann, 2008 .

University of Groningen Adaptive Matrix Metrics for Attribute Dependence Analysis in Differential High-Throughput Data Strickert,

M. Biehl, J. Keilwagen, T. Villmann, 2008 .

Hierarchical Deconvolution of Linear Mixtures of High-Dimensional Mass Spectra in Microbiology

T. Villmann, Frank-Michael Schleif, Stephan Simmuteit, 2011 .

Prototype-based models in machine learning.

Michael Biehl, T. Villmann, B. Hammer, 2016, Wiley interdisciplinary reviews. Cognitive science.

Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2010, Knowledge and Information Systems.

Explicit Magnification Control of Self-Organizing Maps for “Forbidden” Data

Thomas Villmann, Erzsébet Merényi, Abha Jain, 2007, IEEE Transactions on Neural Networks.

Hierarchical evolving trees together with global and local learning for large data sets in MALDI imaging

Frank-Michael Schleif, T. Villmann, Stephan Simmuteit, 2010 .

Estimating Relevant Input Dimensions for Self-organizing Algorithms

Thomas Villmann, Barbara Hammer, T. Villmann, 2001, WSOM.

Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning

Thomas Villmann, Andrea Villmann, Marika Kaden, 2018, ICAISC.

Multi-class and Cluster Evaluation Measures Based on Rényi and Tsallis Entropies and Mutual Information

Thomas Villmann, Tina Geweniger, T. Villmann, 2018, ICAISC.

Information theory related learning

Thomas Villmann, Andrzej Cichocki, José Carlos Príncipe, 2011, ESANN.

Stationarity of Matrix Relevance LVQ

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

The Resolved Mutual Information Function for Characterization of Spatial Correlations in Sequences and Labeled Graphs

Katrin Sophie Bohnsack, T. Villmann, M. Kaden, 2021 .

Neural networks and machine learning in bioinformatics - theory and applications

Thomas Villmann, Barbara Hammer, Samuel Kaski, 2006, ESANN.

Robust Centroid-Based Clustering using Derivatives of Pearson Correlation

Thomas Villmann, Barbara Hammer, Marc Strickert, 2008, BIOSIGNALS.

Divergence based vector quantization of spectral data

Thomas Villmann, Sven Haase, T. Villmann, 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

Divergence Based Online Learning in Vector Quantization

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2010, ICAISC.

Divergence based Learning Vector Quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2010, ESANN.

Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data

Thomas Villmann, Frank-Michael Schleif, Marc Strickert, 2008, Inteligencia Artif..

Building the library of RNA 3D nucleotide conformations using the clustering approach

Thomas Villmann, Jacek Blazewicz, Martin Riedel, 2015, Int. J. Appl. Math. Comput. Sci..

Visualization of processes in self-learning systems

Thomas Villmann, Michael Biehl, Marc Strickert, 2012, 2012 Tenth Annual International Conference on Privacy, Security and Trust.

Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization

Thomas Villmann, Axel Wismüller, Michael Biehl, 2010, ESANN.

Sparse representation of data

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2010, ESANN.

Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization

Thomas Villmann, Dietlind Zühlke, Mandy Lange, 2014, ESANN.

Rule Extraction from Self-Organizing Networks

Thomas Villmann, Barbara Hammer, Marc Strickert, 2002, ICANN.