Thomas Villmann

ORCID

Publication Activity (10 Years)

Years Active: 1993-2023
Publications (10 Years): 84

Top Venues

Leipzig University, Germany (PHD)

Education History

Leipzig University, Germany

PHD

Publications

Katrin Sophie Bohnsack, Julius Voigt, Marika Kaden, Florian Heinke, Thomas Villmann
Multi-proximity based embedding scheme for learning vector quantization-based classification of biochemical structured data. Neurocomputing 554 (2023)
Ronny Schubert, Lynn V. Reuss, Daniel Staps, Marika Kaden, Thomas Villmann, Robert Hasler, Robin Herz, Till Tiemann, Wolfram Richardt
A White-Box Workflow for the Prediction of Food Content From Near-Infrared Data Based on Fourier-Transformation. WHISPERS (2023)
Hanno Stage, Lukas Ewecker, Jacob Langner, Tin Stribor Sohn, Thomas Villmann, Eric Sax
Reducing Computer Vision Dataset Size via Selective Sampling. ITSC (2023)
Katrin Sophie Bohnsack, Marika Kaden, Julia Abel, Thomas Villmann
Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective. IEEE ACM Trans. Comput. Biol. Bioinform. 20 (1) (2023)
Daniel Staps, Marika Kaden, Jan Auth, Florian Zaussinger, Thomas Villmann
Compression of Particle Images for Inspection of Microgravity Experiments by Means of a Symmetric Structural Auto-Encoder. WHISPERS (2023)
Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann, Thomas Villmann
The Geometry of Decision Borders Between Affine Space Prototypes for Nearest Prototype Classifiers. ICAISC (1) (2023)
Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann
An Interpretable Two-Layered Neural Network Structure-Based on Component-Wise Reasoning. ICAISC (1) (2023)
Katrin Sophie Bohnsack, Alexander Engelsberger, Marika Kaden, Thomas Villmann
Efficient Representation of Biochemical Structures for Supervised and Unsupervised Machine Learning Models Using Multi-Sensoric Embeddings. BIOINFORMATICS (2023)
Alexander Engelsberger, Thomas Villmann
Quantum Computing Approaches for Vector Quantization - Current Perspectives and Developments. Entropy 25 (3) (2023)
Paulo J. G. Lisboa, Sascha Saralajew, Alfredo Vellido, Ricardo Fernández-Domenech, Thomas Villmann
The coming of age of interpretable and explainable machine learning models. Neurocomputing 535 (2023)
Katrin Sophie Bohnsack, Marika Kaden, Julius Voigt, Thomas Villmann
Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features. ESANN (2022)
Thomas Villmann, Daniel Staps, Jensun Ravichandran, Sascha Saralajew, Michael Biehl, Marika Kaden
A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. IDA (2022)
Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann
Classification by Components Including Chow's Reject Option. ICONIP (4) (2022)
Marika Kaden, Katrin Sophie Bohnsack, Mirko Weber, Mateusz Kudla, Kaja Gutowska, Jacek Blazewicz, Thomas Villmann
Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences. Neural Comput. Appl. 34 (1) (2022)
Thomas Villmann, Alexander Engelsberger
Multilayer Perceptrons with Banach-Like Perceptrons Based on Semi-inner Products - About Approximation Completeness. ICAISC (1) (2022)
Thomas Villmann, Jonas S. Almeida, Lee John, Susana Vinga
Tutorial - Machine Learning and Information Theoretic Methods for Molecular Biology and Medicine. ESANN (2022)
Jensun Ravichandran, Marika Kaden, Thomas Villmann
Variants of recurrent learning vector quantization. Neurocomputing 502 (2022)
Daniel Staps, Ronny Schubert, Marika Kaden, Alexander Lampe, Wieland Hermann, Thomas Villmann
Prototype-based One-Class-Classification Learning Using Local Representations. IJCNN (2022)
Danny Möbius, Jensun Ravichandran, Marika Kaden, Thomas Villmann
Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models. ICONIP (2) (2022)
Thomas Villmann, Alexander Engelsberger, Jensun Ravichandran, Andrea Villmann, Marika Kaden
Quantum-inspired learning vector quantizers for prototype-based classification. Neural Comput. Appl. 34 (1) (2022)
Mateusz Kudla, Kaja Gutowska, Jaroslaw Synak, Mirko Weber, Katrin Sophie Bohnsack, Piotr Lukasiak, Thomas Villmann, Jacek Blazewicz, Marta Szachniuk
Virxicon: a lexicon of viral sequences. Bioinform. 36 (22-23) (2021)
Seyedfakhredin Musavishavazi, Marika Kaden, Thomas Villmann
Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks. ICAISC (1) (2021)
Thomas Villmann, Alexander Engelsberger
Quantum-Hybrid Neural Vector Quantization - A Mathematical Approach. ICAISC (1) (2021)
Katrin Sophie Bohnsack, Marika Kaden, Julia Abel, Sascha Saralajew, Thomas Villmann
The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers. Entropy 23 (10) (2021)
Marika Kaden, Ronny Schubert, Mehrdad Mohannazadeh Bakhtiari, Lucas Schwarz, Thomas Villmann
The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach. ESANN (2021)
Jan Badura, Artur Laskowski, Maciej Antczak, Jacek Blazewicz, Grzegorz Pawlak, Erwin Pesch, Thomas Villmann, Szymon Wasik
Brilliant Challenges Optimization Problem Submission Contest Final Report. CoRR (2021)
Jensun Ravichandran, Thomas Villmann, Marika Kaden
RecLVQ: Recurrent Learning Vector Quantization. ESANN (2021)
Feryel Zoghlami, Marika Kaden, Thomas Villmann, Germar Schneider, Harald Heinrich
AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task. Sensors 21 (13) (2021)
Feryel Zoghlami, Marika Kaden, Thomas Villmann, Germar Schneider, Harald Heinrich
Sensors data fusion for smart decisions making: A novel bi-functional system for the evaluation of sensors contribution in classification problems. ICIT (2021)
Feryel Zoghlami, Okan Kamil Sen, Harald Heinrich, Germar Schneider, Emec Ercelik, Alois C. Knoll, Thomas Villmann
ToF/Radar early feature-based fusion system for human detection and tracking. ICIT (2021)
Paulo Lisboa, Sascha Saralajew, Alfredo Vellido, Thomas Villmann
The Coming of Age of Interpretable and Explainable Machine Learning Models. ESANN (2021)
Sascha Saralajew, Lars Holdijk, Thomas Villmann
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms. NeurIPS (2020)
Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert
Learning vector quantization and relevances in complex coefficient space. Neural Comput. Appl. 32 (24) (2020)
Seyedfakhredin Musavishavazi, Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann
A Mathematical Model for Optimum Error-Reject Trade-Off for Learning of Secure Classification Models in the Presence of Label Noise During Training. ICAISC (1) (2020)
Jensun Ravichandran, Marika Kaden, Sascha Saralajew, Thomas Villmann
Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability. Neurocomputing 403 (2020)
Thomas Villmann, Jensun Ravichandran, Alexander Engelsberger, Andrea Villmann, Marika Kaden
Quantum-Inspired Learning Vector Quantization for Classification Learning. ESANN (2020)
Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components. NeurIPS (2019)
Tina Geweniger, Thomas Villmann
Variants of Fuzzy Neural Gas. WSOM+ (2019)
Thomas Villmann, Marika Kaden, Szymon Wasik, Mateusz Kudla, Kaja Gutowska, Andrea Villmann, Jacek Blazewicz
Searching for the Origins of Life - Detecting RNA Life Signatures Using Learning Vector Quantization. WSOM+ (2019)
Thomas Villmann, Marika Kaden, Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann
Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis. ICAISC (2) (2019)
Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann
Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks. WSOM+ (2019)
Jensun Ravichandran, Sascha Saralajew, Thomas Villmann
DropConnect for Evaluation of Classification Stability in Learning Vector Quantization. ESANN (2019)
Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann
Statistical physics of learning and inference. ESANN (2019)
Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks. CoRR (2019)
Thomas Villmann, Jensun Ravichandran, Andrea Villmann, David Nebel, Marika Kaden
Investigation of Activation Functions for Generalized Learning Vector Quantization. WSOM+ (2019)
Sebastian Bittrich, Marika Kaden, Christoph Leberecht, Florian Kaiser, Thomas Villmann, Dirk Labudde
Application of an interpretable classification model on Early Folding Residues during protein folding. BioData Min. 12 (1) (2019)
Thomas Villmann, John Ravichandran, Andrea Villmann, David Nebel, Marika Kaden
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison. CoRR (2019)
Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann
Prototype-based Neural Network Layers: Incorporating Vector Quantization. CoRR (2018)
Andrea Villmann, Marika Kaden, Sascha Saralajew, Thomas Villmann
Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. ICAISC (1) (2018)
Thomas Villmann, Tina Geweniger
Multi-class and Cluster Evaluation Measures Based on Rényi and Tsallis Entropies and Mutual Information. ICAISC (1) (2018)
Thomas Villmann, Marika Kaden, Wieland Hermann, Michael Biehl
Learning vector quantization classifiers for ROC-optimization. Comput. Stat. 33 (3) (2018)
Thomas Villmann
Learning Vector Quantization Methods for Interpretable Classification Learning and Multilayer Networks. IJCCI (2018)
Falko Lischke, Thomas Neumann, Sven Hellbach, Thomas Villmann, Hans-Joachim Böhme
Direct Incorporation of L_1 -Regularization into Generalized Matrix Learning Vector Quantization. ICAISC (1) (2018)
Andrea Villmann, Marika Kaden, Sascha Saralajew, Wieland Hermann, Thomas Villmann
Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. ESANN (2018)
Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke
Biomedical data analysis in translational research: integration of expert knowledge and interpretable models. ESANN (2017)
Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert
Prototypes and matrix relevance learning in complex fourier space. WSOM (2017)
Sascha Saralajew, Thomas Villmann
Transfer learning in classification based on manifolc. models and its relation to tangent metric learning. IJCNN (2017)
Marika Kaden, David Nebel, Friedrich Melchert, Andreas Backhaus, Udo Seiffert, Thomas Villmann
Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities. WSOM (2017)
Thomas Villmann, Andrea Bohnsack, Marika Kaden
Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning. J. Artif. Intell. Soft Comput. Res. 7 (1) (2017)
Tina Geweniger, Thomas Villmann
Relational and median variants of Possibilistic Fuzzy C-Means. WSOM (2017)
Thomas Villmann, Michael Biehl, Andrea Villmann, Sascha Saralajew
Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. WSOM (2017)
Mohammad Mohammadi, Michael Biehl, Andrea Villmann, Thomas Villmann
Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. ICAISC (1) (2017)
David Nebel, Marika Kaden, Andrea Villmann, Thomas Villmann
Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning. Neurocomputing 268 (2017)
Sascha Saralajew, David Nebel, Thomas Villmann
Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning. ICONIP (3) (2016)
Andrea Bohnsack, Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann
Learning matrix quantization and relevance learning based on Schatten-p-norms. Neurocomputing 192 (2016)
Thomas Villmann, Marika Kaden, David Nebel, Andrea Bohnsack
Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning - A Mathematical Characterization. ICAISC (2) (2016)
Michael Biehl, Barbara Hammer, Thomas Villmann
Prototype-based Models for the Supervised Learning of Classification Schemes. Astroinformatics (2016)
Thomas Villmann, Marika Kaden, Andrea Bohnsack, J.-M. Villmann, T. Drogies, Sascha Saralajew, Barbara Hammer
Self-Adjusting Reject Options in Prototype Based Classification. WSOM (2016)
Sascha Saralajew, Thomas Villmann
Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning. IJCNN (2016)
David Nebel, Thomas Villmann
Optimization of Statistical Evaluation Measures for Classification by Median Learning Vector Quantization. WSOM (2016)
Matthias Gay, Marika Kaden, Michael Biehl, Alexander Lampe, Thomas Villmann
Complex Variants of GLVQ Based on Wirtinger's Calculus. WSOM (2016)
Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke
Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261). Dagstuhl Reports 6 (6) (2016)
Marika Kaden, David Nebel, Thomas Villmann
Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities. ESANN (2016)
Mandy Lange, Michael Biehl, Thomas Villmann
Non-Euclidean principal component analysis by Hebbian learning. Neurocomputing 147 (2015)
Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann
Learning matrix quantization and variants of relevance learning. ESANN (2015)
Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
Stationarity of Matrix Relevance LVQ. IJCNN (2015)
Marika Kaden, Martin Riedel, Wieland Hermann, Thomas Villmann
Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines. Soft Comput. 19 (9) (2015)
Andrea Bohnsack, Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann
Mathematical Characterization of Sophisticated Variants for Relevance Learning in Learning Matrix Quantization Based on Schatten-p-norms. ICAISC (1) (2015)
Thomas Villmann
Sophisticated LVQ Classification Models - Beyond Accuracy Optimization. BrainComp (2015)
Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl
Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. CAIP (2) (2015)
David Nebel, Thomas Villmann
Median-LVQ for classification of dissimilarity data based on ROC-optimization. ESANN (2015)
Thomas Villmann, Sven Haase, Marika Kaden
Kernelized vector quantization in gradient-descent learning. Neurocomputing 147 (2015)
Tomasz Zok, Maciej Antczak, Martin Riedel, David Nebel, Thomas Villmann, Piotr Lukasiak, Jacek Blazewicz, Marta Szachniuk
Building the Library of Rna 3D Nucleotide Conformations Using the Clustering Approach. Int. J. Appl. Math. Comput. Sci. 25 (3) (2015)
David Nebel, Barbara Hammer, Kathleen Frohberg, Thomas Villmann
Median variants of learning vector quantization for learning of dissimilarity data. Neurocomputing 169 (2015)
Mandy Lange, David Nebel, Thomas Villmann
Non-euclidean Principal Component Analysis for Matrices by Hebbian Learning. ICAISC (1) (2014)
Thomas Villmann, Marika Kaden, Mandy Lange, Paul Sturmer, Wieland Hermann
Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems. CIDM (2014)
Mathias Klingner, Sven Hellbach, Martin Riedel, Marika Kaden, Thomas Villmann, Hans-Joachim Böhme
RFSOM - Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation. WSOM (2014)
Mandy Lange, Dietlind Zühlke, Olaf Holz, Thomas Villmann
Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization. ESANN (2014)
Lydia Fischer, David Nebel, Thomas Villmann, Barbara Hammer, Heiko Wersing
Rejection Strategies for Learning Vector Quantization - A Comparison of Probabilistic and Deterministic Approaches. WSOM (2014)
Thomas Villmann, Marika Kaden, David Nebel, Martin Riedel
Lateral enhancement in adaptive metric learning for functional data. Neurocomputing 131 (2014)
Mandy Lange, David Nebel, Thomas Villmann
Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization. WSOM (2014)
Sven Hellbach, Marian Himstedt, Frank Bahrmann, Martin Riedel, Thomas Villmann, Hans-Joachim Böhme
Some Room for GLVQ: Semantic Labeling of Occupancy Grid Maps. WSOM (2014)
Marika Kaden, Wieland Hermann, Thomas Villmann
Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment. WSOM (2014)
Barbara Hammer, David Nebel, Martin Riedel, Thomas Villmann
Generative versus Discriminative Prototype Based Classification. WSOM (2014)
Frank-Michael Schleif, Peter Tiño, Thomas Villmann
Recent trends in learning of structured and non-standard data. ESANN (2014)
Frank-Michael Schleif, Thomas Villmann, Xibin Zhu
High Dimensional Matrix Relevance Learning. ICDM Workshops (2014)
Sven Hellbach, Marian Himstedt, Frank Bahrmann, Martin Riedel, Thomas Villmann, Hans-Joachim Böhme
Find Rooms for Improvement: Towards Semi-automatic Labeling of Occupancy Grid Maps. ICONIP (3) (2014)
Kristin Domaschke, André Roßberg, Thomas Villmann
Utilization of Chemical Structure Information for Analysis of Spectra Composites. ESANN (2014)
Marika Kaden, Wieland Hermann, Thomas Villmann
Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization. ESANN (2014)
David Nebel, Barbara Hammer, Thomas Villmann
Supervised Generative Models for Learning Dissimilarity Data. ESANN (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Thomas Villmann