Thomas Villmann

298 papers A* 3A 1B 84C 12Misc 6Journal 82Unranked 100

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	FA(IR)2MA-GLVQ - A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization. Neurocomputing Marika Kaden, Ronny Schubert, Julius Voigt, Lynn V. Reuss, Alexander Engelsberger, Sofie Lövdal, Elina L. van den Brandhof, Michael Biehl, Thomas Villmann
2026	J	`jnl`	Network reliability analysis by means of generalized matrix learning vector quantization. Appl. Netw. Sci. Klaus Dohmen, Mandy Lange-Geisler, Thomas Villmann
2026	J	`jnl`	Privacy-preserving nearest prototype classifier. Neurocomputing Ronny Schubert, Thomas Davies, Mandy Lange-Geisler, Klaus Dohmen, Thomas Villmann
2026	J	`jnl`	Prototype-Based Classifiers and Vector Quantization on a Quantum Computer - Implementing Integer Arithmetic Oracles for Nearest Prototype Search. Entropy Alexander Engelsberger, M. Psenickova, Thomas Villmann
2025	A*	`conf`	A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations. AAAI Sascha Saralajew, Ashish Rana, Thomas Villmann, Ammar Shaker
2025	—	`conf`	Prototype Learning for Classification on Spherical Manifolds and Its Relation to Quantum Classification Approaches. ICAISC (1) Alexander Engelsberger, M. Psenickova, Thomas Villmann
2025	—	`conf`	Reliable Classification Learning for Medical Data Analysis Using Prototype-Based Models. ICAISC (1) Julius Voigt, Marika Kaden, Lynn V. Reuss, Thomas Villmann
2024	J	`jnl`	A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations. CoRR Sascha Saralajew, Ashish Rana, Thomas Villmann, Ammar Shaker
2024	—	`conf`	About Interpretable Learning Rules for Vector Quantizers - A Methodological Approach. WSOM+ Ronny Schubert, Thomas Villmann
2024	B	`conf`	About Vector Quantization and its Privacy in Federated Learning. ESANN Ronny Schubert, Thomas Villmann
2024	—	`ed.`	Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond - Proceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024 WSOM+ Thomas Villmann, Marika Kaden, Tina Geweniger, Frank-Michael Schleif
2024	J	`jnl`	Aligning Generalisation Between Humans and Machines. CoRR 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, Pasquale Minervini, Martin Mundt, Axel-Cyrille Ngonga Ngomo, Alessandro Oltramari, Gabriella Pasi, Zeynep G. Saribatur, Luciano Serafini, John Shawe-Taylor, Vered Shwartz, Gabriella Skitalinskaya, Clemens Stachl, Gido M. van de Ven, Thomas Villmann
2024	—	`conf`	Are Gamers Good Annotators? A Comparative Study of Gaming Crowds and Professional Annotators. ITSC Lukas Ewecker, Christopher Klugmann, Daniel Kondermann, Robin Schwager, Thomas Villmann
2024	—	`conf`	Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge. BIOSTEC (1) Julius Voigt, Sascha Saralajew, Marika Kaden, Katrin Sophie Bohnsack, Lynn V. Reuss, Thomas Villmann
2024	B	`conf`	Domain Knowledge Integration in Machine Learning Systems - An Introduction. ESANN Marika Kaden, Sascha Saralajew, Thomas Villmann
2024	—	`conf`	Hyperbox-GLVQ Based on Min-Max-Neurons. WSOM+ Thomas Villmann, T. Davies, Alexander Engelsberger
2024	—	`conf`	K Minimum Enclosing Balls for Outlier Detection. WSOM+ Daniel Staps, Thomas Villmann, Benjamin Paaßen
2024	—	`conf`	Knowledge Integration in Vector Quantization Models and Corresponding Structured Covariance Estimation. WSOM+ Marika Kaden, Julius Voigt, Katrin Sophie Bohnsack, Mandy Lange-Geisler, Thomas Villmann
2024	B	`conf`	PVDN-Urban - A Dataset for Provident Vehicle Detection at Night in Urban Scenarios. ICIP Lukas Ewecker, Florian Schiffel, Robin Schwager, Tim Brühl, Tin Stribor Sohn, Thomas Villmann
2024	J	`jnl`	Subspace corrected relevance learning with application in neuroimaging. Artif. Intell. Medicine 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, Thomas Villmann, Michael Biehl
2023	—	`conf`	A White-Box Workflow for the Prediction of Food Content From Near-Infrared Data Based on Fourier-Transformation. WHISPERS Ronny Schubert, Lynn V. Reuss, Daniel Staps, Marika Kaden, Thomas Villmann, Robert Hasler, Robin Herz, Till Tiemann, Wolfram Richardt
2023	J	`jnl`	Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective. IEEE ACM Trans. Comput. Biol. Bioinform. Katrin Sophie Bohnsack, Marika Kaden, Julia Abel, Thomas Villmann
2023	—	`conf`	An Interpretable Two-Layered Neural Network Structure-Based on Component-Wise Reasoning. ICAISC (1) Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann
2023	—	`conf`	Compression of Particle Images for Inspection of Microgravity Experiments by Means of a Symmetric Structural Auto-Encoder. WHISPERS Daniel Staps, Marika Kaden, Jan Auth, Florian Zaussinger, Thomas Villmann
2023	—	`conf`	Efficient Representation of Biochemical Structures for Supervised and Unsupervised Machine Learning Models Using Multi-Sensoric Embeddings. BIOINFORMATICS Katrin Sophie Bohnsack, Alexander Engelsberger, Marika Kaden, Thomas Villmann
2023	B	`conf`	Learning Vector Quantization in Context of Information Bottleneck Theory. ESANN Mehrdad Mohannazadeh Bakhtiari, Daniel Staps, Thomas Villmann
2023	J	`jnl`	Multi-proximity based embedding scheme for learning vector quantization-based classification of biochemical structured data. Neurocomputing Katrin Sophie Bohnsack, Julius Voigt, Marika Kaden, Florian Heinke, Thomas Villmann
2023	B	`conf`	Quantum Artificial Intelligence: A tutorial. ESANN José D. Martín-Guerrero, Lucas Lamata, Thomas Villmann
2023	J	`jnl`	Quantum Computing Approaches for Vector Quantization - Current Perspectives and Developments. Entropy Alexander Engelsberger, Thomas Villmann
2023	B	`conf`	Quantum-ready vector quantization: Prototype learning as a binary optimization problem. ESANN Alexander Engelsberger, Thomas Villmann
2023	—	`conf`	Reducing Computer Vision Dataset Size via Selective Sampling. ITSC Hanno Stage, Lukas Ewecker, Jacob Langner, Tin Stribor Sohn, Thomas Villmann, Eric Sax
2023	B	`conf`	Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection. ESANN Maximilian Münch, Katrin Sophie Bohnsack, Alexander Engelsberger, Frank-Michael Schleif, Thomas Villmann
2023	—	`conf`	The Geometry of Decision Borders Between Affine Space Prototypes for Nearest Prototype Classifiers. ICAISC (1) Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann, Thomas Villmann
2023	J	`jnl`	The coming of age of interpretable and explainable machine learning models. Neurocomputing Paulo J. G. Lisboa, Sascha Saralajew, Alfredo Vellido, Ricardo Fernández-Domenech, Thomas Villmann
2023	B	`conf`	Variants of Neural Gas for Regression Learning. ESANN Thomas Villmann, Ronny Schubert, Marika Kaden
2022	B	`conf`	A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. IDA Thomas Villmann, Daniel Staps, Jensun Ravichandran, Sascha Saralajew, Michael Biehl, Marika Kaden
2022	—	`conf`	Classification by Components Including Chow's Reject Option. ICONIP (4) Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann
2022	B	`conf`	Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features. ESANN Katrin Sophie Bohnsack, Marika Kaden, Julius Voigt, Thomas Villmann
2022	J	`jnl`	Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences. Neural Comput. Appl. Marika Kaden, Katrin Sophie Bohnsack, Mirko Weber, Mateusz Kudla, Kaja Gutowska, Jacek Blazewicz, Thomas Villmann
2022	—	`conf`	Multilayer Perceptrons with Banach-Like Perceptrons Based on Semi-inner Products - About Approximation Completeness. ICAISC (1) Thomas Villmann, Alexander Engelsberger
2022	B	`conf`	Prototype-based One-Class-Classification Learning Using Local Representations. IJCNN Daniel Staps, Ronny Schubert, Marika Kaden, Alexander Lampe, Wieland Hermann, Thomas Villmann
2022	J	`jnl`	Quantum-inspired learning vector quantizers for prototype-based classification. Neural Comput. Appl. Thomas Villmann, Alexander Engelsberger, Jensun Ravichandran, Andrea Villmann, Marika Kaden
2022	—	`conf`	Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models. ICONIP (2) Danny Möbius, Jensun Ravichandran, Marika Kaden, Thomas Villmann
2022	B	`conf`	Tutorial - Machine Learning and Information Theoretic Methods for Molecular Biology and Medicine. ESANN Thomas Villmann, Jonas S. Almeida, John A. Lee, Susana Vinga
2022	J	`jnl`	Variants of recurrent learning vector quantization. Neurocomputing Jensun Ravichandran, Marika Kaden, Thomas Villmann
2021	J	`jnl`	AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task. Sensors Feryel Zoghlami, Marika Kaden, Thomas Villmann, Germar Schneider, Harald Heinrich
2021	J	`jnl`	Brilliant Challenges Optimization Problem Submission Contest Final Report. CoRR Jan Badura, Artur Laskowski, Maciej Antczak, Jacek Blazewicz, Grzegorz Pawlak, Erwin Pesch, Thomas Villmann, Szymon Wasik
2021	—	`conf`	Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks. ICAISC (1) Seyedfakhredin Musavishavazi, Marika Kaden, Thomas Villmann
2021	—	`conf`	Quantum-Hybrid Neural Vector Quantization - A Mathematical Approach. ICAISC (1) Thomas Villmann, Alexander Engelsberger
2021	B	`conf`	RecLVQ: Recurrent Learning Vector Quantization. ESANN Jensun Ravichandran, Thomas Villmann, Marika Kaden
2021	—	`conf`	Sensors data fusion for smart decisions making: A novel bi-functional system for the evaluation of sensors contribution in classification problems. ICIT Feryel Zoghlami, Marika Kaden, Thomas Villmann, Germar Schneider, Harald Heinrich
2021	B	`conf`	The Coming of Age of Interpretable and Explainable Machine Learning Models. ESANN Paulo Lisboa, Sascha Saralajew, Alfredo Vellido, Thomas Villmann
2021	B	`conf`	The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach. ESANN Marika Kaden, Ronny Schubert, Mehrdad Mohannazadeh Bakhtiari, Lucas Schwarz, Thomas Villmann
2021	J	`jnl`	The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers. Entropy Katrin Sophie Bohnsack, Marika Kaden, Julia Abel, Sascha Saralajew, Thomas Villmann
2021	—	`conf`	ToF/Radar early feature-based fusion system for human detection and tracking. ICIT Feryel Zoghlami, Okan Kamil Sen, Harald Heinrich, Germar Schneider, Emec Ercelik, Alois C. Knoll, Thomas Villmann
2021	J	`jnl`	Virxicon: a lexicon of viral sequences. Bioinform. Mateusz Kudla, Kaja Gutowska, Jaroslaw Synak, Mirko Weber, Katrin Sophie Bohnsack, Piotr Lukasiak, Thomas Villmann, Jacek Blazewicz, Marta Szachniuk
2020	—	`conf`	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) Seyedfakhredin Musavishavazi, Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann
2020	A*	`conf`	Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms. NeurIPS Sascha Saralajew, Lars Holdijk, Thomas Villmann
2020	J	`jnl`	Learning vector quantization and relevances in complex coefficient space. Neural Comput. Appl. Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert
2020	B	`conf`	Quantum-Inspired Learning Vector Quantization for Classification Learning. ESANN Thomas Villmann, Jensun Ravichandran, Alexander Engelsberger, Andrea Villmann, Marika Kaden
2020	J	`jnl`	Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability. Neurocomputing Jensun Ravichandran, Marika Kaden, Sascha Saralajew, Thomas Villmann
2019	J	`jnl`	Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison. CoRR Thomas Villmann, John Ravichandran, Andrea Villmann, David Nebel, Marika Kaden
2019	J	`jnl`	Application of an interpretable classification model on Early Folding Residues during protein folding. BioData Min. Sebastian Bittrich, Marika Kaden, Christoph Leberecht, Florian Kaiser, Thomas Villmann, Dirk Labudde
2019	—	`conf`	Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis. ICAISC (2) Thomas Villmann, Marika Kaden, Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann
2019	A*	`conf`	Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components. NeurIPS Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann
2019	B	`conf`	DropConnect for Evaluation of Classification Stability in Learning Vector Quantization. ESANN Jensun Ravichandran, Sascha Saralajew, Thomas Villmann
2019	—	`conf`	Investigation of Activation Functions for Generalized Learning Vector Quantization. WSOM+ Thomas Villmann, Jensun Ravichandran, Andrea Villmann, David Nebel, Marika Kaden
2019	—	`conf`	Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks. WSOM+ Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann
2019	J	`jnl`	Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks. CoRR Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann
2019	—	`conf`	Searching for the Origins of Life - Detecting RNA Life Signatures Using Learning Vector Quantization. WSOM+ Thomas Villmann, Marika Kaden, Szymon Wasik, Mateusz Kudla, Kaja Gutowska, Andrea Villmann, Jacek Blazewicz
2019	B	`conf`	Statistical physics of learning and inference. ESANN Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann
2019	—	`conf`	Variants of Fuzzy Neural Gas. WSOM+ Tina Geweniger, Thomas Villmann
2018	—	`conf`	Direct Incorporation of L_1 -Regularization into Generalized Matrix Learning Vector Quantization. ICAISC (1) Falko Lischke, Thomas Neumann, Sven Hellbach, Thomas Villmann, Hans-Joachim Böhme
2018	C	`conf`	Learning Vector Quantization Methods for Interpretable Classification Learning and Multilayer Networks. IJCCI Thomas Villmann
2018	J	`jnl`	Learning vector quantization classifiers for ROC-optimization. Comput. Stat. Thomas Villmann, Marika Kaden, Wieland Hermann, Michael Biehl
2018	—	`conf`	Multi-class and Cluster Evaluation Measures Based on Rényi and Tsallis Entropies and Mutual Information. ICAISC (1) Thomas Villmann, Tina Geweniger
2018	—	`conf`	Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. ICAISC (1) Andrea Villmann, Marika Kaden, Sascha Saralajew, Thomas Villmann
2018	J	`jnl`	Prototype-based Neural Network Layers: Incorporating Vector Quantization. CoRR Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann
2018	B	`conf`	Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. ESANN Andrea Villmann, Marika Kaden, Sascha Saralajew, Wieland Hermann, Thomas Villmann
2017	B	`conf`	Biomedical data analysis in translational research: integration of expert knowledge and interpretable models. ESANN Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke
2017	J	`jnl`	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. Thomas Villmann, Andrea Bohnsack, Marika Kaden
2017	—	`conf`	Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities. WSOM Marika Kaden, David Nebel, Friedrich Melchert, Andreas Backhaus, Udo Seiffert, Thomas Villmann
2017	—	`conf`	Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. WSOM Thomas Villmann, Michael Biehl, Andrea Villmann, Sascha Saralajew
2017	—	`conf`	Prototypes and matrix relevance learning in complex fourier space. WSOM Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert
2017	—	`conf`	Relational and median variants of Possibilistic Fuzzy C-Means. WSOM Tina Geweniger, Thomas Villmann
2017	—	`conf`	Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. ICAISC (1) Mohammad Mohammadi, Michael Biehl, Andrea Villmann, Thomas Villmann
2017	B	`conf`	Transfer learning in classification based on manifolc. models and its relation to tangent metric learning. IJCNN Sascha Saralajew, Thomas Villmann
2017	J	`jnl`	Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning. Neurocomputing David Nebel, Marika Kaden, Andrea Villmann, Thomas Villmann
2016	—	`conf`	Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning. ICONIP (3) Sascha Saralajew, David Nebel, Thomas Villmann
2016	B	`conf`	Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities. ESANN Marika Kaden, David Nebel, Thomas Villmann
2016	B	`conf`	Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning. IJCNN Sascha Saralajew, Thomas Villmann
2016	—	`conf`	Complex Variants of GLVQ Based on Wirtinger's Calculus. WSOM Matthias Gay, Marika Kaden, Michael Biehl, Alexander Lampe, Thomas Villmann
2016	J	`jnl`	Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261). Dagstuhl Reports Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke
2016	J	`jnl`	Learning matrix quantization and relevance learning based on Schatten-p-norms. Neurocomputing Andrea Bohnsack, Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann
2016	—	`conf`	Optimization of Statistical Evaluation Measures for Classification by Median Learning Vector Quantization. WSOM David Nebel, Thomas Villmann
2016	—	`conf`	Prototype-based Models for the Supervised Learning of Classification Schemes. Astroinformatics Michael Biehl, Barbara Hammer, Thomas Villmann
2016	—	`conf`	Self-Adjusting Reject Options in Prototype Based Classification. WSOM Thomas Villmann, Marika Kaden, Andrea Bohnsack, J.-M. Villmann, T. Drogies, Sascha Saralajew, Barbara Hammer
2016	—	`conf`	Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning - A Mathematical Characterization. ICAISC (2) Thomas Villmann, Marika Kaden, David Nebel, Andrea Bohnsack
2015	J	`jnl`	Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines. Soft Comput. Marika Kaden, Martin Riedel, Wieland Hermann, Thomas Villmann
2015	J	`jnl`	Building the Library of Rna 3D Nucleotide Conformations Using the Clustering Approach. Int. J. Appl. Math. Comput. Sci. Tomasz Zok, Maciej Antczak, Martin Riedel, David Nebel, Thomas Villmann, Piotr Lukasiak, Jacek Blazewicz, Marta Szachniuk
2015	J	`jnl`	Kernelized vector quantization in gradient-descent learning. Neurocomputing Thomas Villmann, Sven Haase, Marika Kaden
2015	—	`conf`	Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. CAIP (2) Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl
2015	B	`conf`	Learning matrix quantization and variants of relevance learning. ESANN Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann
2015	—	`conf`	Mathematical Characterization of Sophisticated Variants for Relevance Learning in Learning Matrix Quantization Based on Schatten-p-norms. ICAISC (1) Andrea Bohnsack, Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann
2015	J	`jnl`	Median variants of learning vector quantization for learning of dissimilarity data. Neurocomputing David Nebel, Barbara Hammer, Kathleen Frohberg, Thomas Villmann
2015	B	`conf`	Median-LVQ for classification of dissimilarity data based on ROC-optimization. ESANN David Nebel, Thomas Villmann
2015	J	`jnl`	Non-Euclidean principal component analysis by Hebbian learning. Neurocomputing Mandy Lange, Michael Biehl, Thomas Villmann
2015	—	`ch.`	Probabilistic Modeling in Machine Learning. Handbook of Computational Intelligence Davide Bacciu, Paulo J. G. Lisboa, Alessandro Sperduti, Thomas Villmann
2015	—	`conf`	Sophisticated LVQ Classification Models - Beyond Accuracy Optimization. BrainComp Thomas Villmann
2015	B	`conf`	Stationarity of Matrix Relevance LVQ. IJCNN Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
2014	—	`ed.`	Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014 WSOM Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange
2014	B	`conf`	Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization. ESANN Mandy Lange, Dietlind Zühlke, Olaf Holz, Thomas Villmann
2014	—	`conf`	Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment. WSOM Marika Kaden, Wieland Hermann, Thomas Villmann
2014	—	`conf`	Find Rooms for Improvement: Towards Semi-automatic Labeling of Occupancy Grid Maps. ICONIP (3) Sven Hellbach, Marian Himstedt, Frank Bahrmann, Martin Riedel, Thomas Villmann, Hans-Joachim Böhme
2014	—	`conf`	Generative versus Discriminative Prototype Based Classification. WSOM Barbara Hammer, David Nebel, Martin Riedel, Thomas Villmann
2014	—	`conf`	High Dimensional Matrix Relevance Learning. ICDM Workshops Frank-Michael Schleif, Thomas Villmann, Xibin Zhu
2014	J	`jnl`	Lateral enhancement in adaptive metric learning for functional data. Neurocomputing Thomas Villmann, Marika Kaden, David Nebel, Martin Riedel
2014	—	`conf`	Non-euclidean Principal Component Analysis for Matrices by Hebbian Learning. ICAISC (1) Mandy Lange, David Nebel, Thomas Villmann
2014	B	`conf`	Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization. ESANN Marika Kaden, Wieland Hermann, Thomas Villmann
2014	—	`conf`	Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization. WSOM Mandy Lange, David Nebel, Thomas Villmann
2014	C	`conf`	Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems. CIDM Thomas Villmann, Marika Kaden, Mandy Lange, Paul Sturmer, Wieland Hermann
2014	—	`conf`	Probabilistic Prototype Classification Using t-norms. WSOM Tina Geweniger, Frank-Michael Schleif, Thomas Villmann
2014	—	`conf`	RFSOM - Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation. WSOM Mathias Klingner, Sven Hellbach, Martin Riedel, Marika Kaden, Thomas Villmann, Hans-Joachim Böhme
2014	B	`conf`	Recent trends in learning of structured and non-standard data. ESANN Frank-Michael Schleif, Peter Tiño, Thomas Villmann
2014	—	`conf`	Rejection Strategies for Learning Vector Quantization - A Comparison of Probabilistic and Deterministic Approaches. WSOM Lydia Fischer, David Nebel, Thomas Villmann, Barbara Hammer, Heiko Wersing
2014	—	`conf`	Some Room for GLVQ: Semantic Labeling of Occupancy Grid Maps. WSOM Sven Hellbach, Marian Himstedt, Frank Bahrmann, Martin Riedel, Thomas Villmann, Hans-Joachim Böhme
2014	J	`jnl`	Special issue on new challenges in neural computation 2012. Neurocomputing Barbara Hammer, Thomas Villmann
2014	B	`conf`	Supervised Generative Models for Learning Dissimilarity Data. ESANN David Nebel, Barbara Hammer, Thomas Villmann
2014	B	`conf`	Utilization of Chemical Structure Information for Analysis of Spectra Composites. ESANN Kristin Domaschke, André Roßberg, Thomas Villmann
2013	—	`conf`	A Median Variant of Generalized Learning Vector Quantization. ICONIP (2) David Nebel, Barbara Hammer, Thomas Villmann
2013	B	`conf`	A sparse kernelized matrix learning vector quantization model for human activity recognition. ESANN Marika Kästner, Marc Strickert, Thomas Villmann
2013	B	`conf`	About analysis and robust classification of searchlight fMRI-data using machine learning classifiers. IJCNN Mandy Lange, Marika Kästner, Thomas Villmann
2013	B	`conf`	Border sensitive fuzzy vector quantization in semi-supervised learning. ESANN Tina Geweniger, Marika Kästner, Thomas Villmann
2013	—	`conf`	Border-Sensitive Learning in Kernelized Learning Vector Quantization. IWANN (1) Marika Kästner, Martin Riedel, Marc Strickert, Wieland Hermann, Thomas Villmann
2013	J	`jnl`	Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters. Comput. Intell. Neurosci. Tina Geweniger, Lydia Fischer, Marika Kaden, Mandy Lange, Thomas Villmann
2013	—	`conf`	Distance Measures for Prototype Based Classification. BrainComp Michael Biehl, Barbara Hammer, Thomas Villmann
2013	J	`jnl`	Editorial A Successful Change From TNN to TNNLS and a Very Successful Year. IEEE Trans. Neural Networks Learn. Syst. Derong Liu, Charles Anderson, Ahmad Taher Azar, Giorgio Battistelli, Eduardo Bayro-Corrochano, Cristiano Cervellera, David A. Elizondo, Maurizio Filippone, Giorgio Gnecco, Xiaolin Hu, Tingwen Huang, Weifeng Liu, Wenlian Lu, Ana Maria Madureira, Igor Skrjanc, Thomas Villmann, Q. M. Jonathan Wu, Shengli Xie, Dong Xu
2013	B	`conf`	Non-Euclidean independent component analysis and Oja's learning. ESANN Mandy Lange, Michael Biehl, Thomas Villmann
2013	B	`conf`	Processing Hyperspectral Data in Machine Learning. ESANN Thomas Villmann, Marika Kästner, Andreas Backhaus, Udo Seiffert
2013	C	`conf`	Regularization and improved interpretation of linear data mappings and adaptive distance measures. CIDM Marc Strickert, Barbara Hammer, Thomas Villmann, Michael Biehl
2013	J	`jnl`	Regularization in Relevance Learning Vector Quantization Using l one Norms. CoRR Martin Riedel, Marika Kästner, Fabrice Rossi, Thomas Villmann
2013	B	`conf`	Regularization in relevance learning vector quantization using l1-norms. ESANN Martin Riedel, Fabrice Rossi, Marika Kästner, Thomas Villmann
2012	—	`conf`	Differentiable Kernels in Generalized Matrix Learning Vector Quantization. ICMLA (1) Marika Kästner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann
2012	J	`jnl`	Functional relevance learning in generalized learning vector quantization. Neurocomputing Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann
2012	—	`conf`	Fuzzy Neural Gas for Unsupervised Vector Quantization. ICAISC (1) Thomas Villmann, Tina Geweniger, Marika Kästner, Mandy Lange
2012	—	`conf`	Fuzzy Supervised Self-Organizing Map for Semi-supervised Vector Quantization. ICAISC (1) Marika Kästner, Thomas Villmann
2012	—	`conf`	Gradient Based Learning in Vector Quantization Using Differentiable Kernels. WSOM Thomas Villmann, Sven Haase, Marika Kästner
2012	—	`conf`	ICMLA Face Recognition Challenge - Results of the Team Computational Intelligence Mittweida. ICMLA (2) Thomas Villmann, Marika Kästner, David Nebel, Martin Riedel
2012	B	`conf`	Integration of Structural Expert Knowledge about Classes for Classification Using the Fuzzy Supervised Neural Gas. ESANN Marika Kästner, Wieland Hermann, Thomas Villmann
2012	B	`conf`	Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
2012	J	`jnl`	Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks Kerstin Bunte, Petra Schneider, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann, Michael Biehl
2012	B	`conf`	Modified Conn-Index for the evaluation of fuzzy clusterings. ESANN Tina Geweniger, Marika Kästner, Mandy Lange, Thomas Villmann
2012	—	`conf`	Non-Euclidean Principal Component Analysis and Oja's Learning Rule - Theoretical Aspects. WSOM Michael Biehl, Marika Kästner, Mandy Lange, Thomas Villmann
2012	B	`conf`	Recent developments in clustering algorithms. ESANN Charles Bouveyron, Barbara Hammer, Thomas Villmann
2012	J	`jnl`	Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. Neurocomputing Kerstin Bunte, Sven Haase, Michael Biehl, Thomas Villmann
2012	B	`conf`	Unmixing Hyperspectral Images with Fuzzy Supervised Self-Organizing Maps. ESANN Thomas Villmann, Erzsébet Merényi, William H. Farrand
2012	C	`conf`	Visualization of processes in self-learning systems. PST Gabriele Peters, Kerstin Bunte, Marc Strickert, Michael Biehl, Thomas Villmann
2011	J	`jnl`	Divergence-Based Vector Quantization. Neural Comput. Thomas Villmann, Sven Haase
2011	J	`jnl`	Divergence-based classification in learning vector quantization. Neurocomputing Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Jennifer R. Aduwo, John A. Quinn, Sven Haase, Thomas Villmann, Michael Biehl
2011	J	`jnl`	Efficient Kernelized Prototype Based Classification. Int. J. Neural Syst. Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider
2011	—	`conf`	Functional relevance learning in learning vector quantization for hyperspectral data. WHISPERS M. Kästner, Thomas Villmann
2011	B	`conf`	Generalized functional relevance learning vector quantization. ESANN Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann
2011	B	`conf`	Information theory related learning. ESANN Thomas Villmann, José C. Príncipe, Andrzej Cichocki
2011	J	`jnl`	Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). Dagstuhl Reports Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, Thomas Villmann
2011	B	`conf`	Magnification in divergence based neural maps. IJCNN Thomas Villmann, Sven Haase
2011	B	`conf`	Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. ESANN Kerstin Bunte, Frank-Michael Schleif, Sven Haase, Thomas Villmann
2011	B	`conf`	Multispectral image characterization by partial generalized covariance. ESANN Marc Strickert, Björn Labitzke, Andreas Kolb, Thomas Villmann
2011	B	`conf`	Multivariate class labeling in Robust Soft LVQ. ESANN Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann
2011	J	`jnl`	Neighbor embedding XOM for dimension reduction and visualization. Neurocomputing Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller
2011	B	`conf`	Optimization of Parametrized Divergences in Fuzzy c-Means. ESANN Tina Geweniger, Marika Kästner, Thomas Villmann
2011	—	`conf`	Relevance Learning in Unsupervised Vector Quantization Based on Divergences. WSOM Marika Kästner, Andreas Backhaus, Tina Geweniger, Sven Haase, Udo Seiffert, Thomas Villmann
2011	—	`conf`	Sparse Functional Relevance Learning in Generalized Learning Vector Quantization. WSOM Thomas Villmann, Marika Kästner
2010	—	`conf`	Cluster Analysis of Cortical Pyramidal Neurons Using SOM. ANNPR Andreas Schierwagen, Thomas Villmann, Alán Alpár, Ulrich Gärtner
2010	—	`conf`	Divergence Based Online Learning in Vector Quantization. ICAISC (1) Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer
2010	B	`conf`	Divergence based Learning Vector Quantization. ESANN Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl
2010	—	`conf`	Divergence based vector quantization of spectral data. WHISPERS Thomas Villmann, Sven Haase
2010	J	`jnl`	Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowl. Inf. Syst. Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2010	B	`conf`	Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. ESANN Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller
2010	B	`conf`	Extending FSNPC to handle data points with fuzzy class assignments. ESANN Tina Geweniger, Thomas Villmann
2010	C	`conf`	Generalized Derivative Based Kernelized Learning Vector Quantization. IDEAL Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl
2010	B	`conf`	Learning vector quantization for heterogeneous structured data. ESANN Dietlind Zühlke, Frank-Michael Schleif, Tina Geweniger, Sven Haase, Thomas Villmann
2010	J	`jnl`	Median fuzzy c-means for clustering dissimilarity data. Neurocomputing Tina Geweniger, Dietlind Zühlke, Barbara Hammer, Thomas Villmann
2010	J	`jnl`	Regularization in matrix relevance learning. IEEE Trans. Neural Networks Petra Schneider, Kerstin Bunte, Han Stiekema, Barbara Hammer, Thomas Villmann, Michael Biehl
2010	B	`conf`	Sparse representation of data. ESANN Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
2010	—	`conf`	The Mathematics of Divergence Based Online Learning in Vector Quantization. ANNPR Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer, Michael Biehl
2009	—	`conf`	09081 Abstracts Collection - Similarity-based learning on structures. Similarity-based learning on structures Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann
2009	—	`conf`	09081 Summary - Similarity-based learning on structures. Similarity-based learning on structures Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann
2009	J	`jnl`	Cancer informatics by prototype networks in mass spectrometry. Artif. Intell. Medicine Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa, Barbara Hammer, Alexander Gammerman
2009	—	`conf`	Functional Principal Component Learning Using Oja's Method and Sobolev Norms. WSOM Thomas Villmann, Barbara Hammer
2009	—	`conf`	Funtional vector quantization by neural maps. WHISPERS Thomas Villmann, Frank-Michael Schleif
2009	B	`conf`	Fuzzy Fleiss-kappa for Comparison of Fuzzy Classifiers. ESANN Dietlind Zühlke, Tina Geweniger, Ulrich Heimann, Thomas Villmann
2009	—	`conf`	Fuzzy Variant of Affinity Propagation in Comparison to Median Fuzzy c-Means. WSOM Tina Geweniger, Dietlind Zühlke, Barbara Hammer, Thomas Villmann
2009	—	`conf`	Hierarchical PCA Using Tree-SOM for the Identification of Bacteria. WSOM Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa
2009	—	`conf`	Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. IWANN (1) Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl
2009	B	`conf`	Median Variant of Fuzzy c-Means. ESANN Tina Geweniger, Dietlind Zühlke, Barbara Hammer, Thomas Villmann
2009	—	`ch.`	Metric Learning for Prototype-Based Classification. Innovations in Neural Information Paradigms and Applications Michael Biehl, Barbara Hammer, Petra Schneider, Thomas Villmann
2009	B	`conf`	Neural Maps and Learning Vector Quantization - Theory and Applications. ESANN Frank-Michael Schleif, Thomas Villmann
2009	—	`ch.`	Prototype Based Classification in Bioinformatics. Encyclopedia of Artificial Intelligence Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2009	—	`ed.`	Similarity-Based Clustering, Recent Developments and Biomedical Applications [outcome of a Dagstuhl Seminar] Similarity-Based Clustering Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann
2009	—	`ed.`	Similarity-based learning on structures, 15.02. - 20.02.2009 Similarity-based learning on structures Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann
2009	—	`conf`	Some Theoretical Aspects of the Neural Gas Vector Quantizer. Similarity-Based Clustering Thomas Villmann, Barbara Hammer, Michael Biehl
2009	J	`jnl`	Supervised data analysis and reliability estimation with exemplary application for spectral data. Neurocomputing Frank-Michael Schleif, Thomas Villmann, Matthias Ongyerth
2009	C	`conf`	Tanimoto Metric in Tree-SOM for Improved Representation of Mass Spectrometry Data with an Underlying Taxonomic Structure. ICMLA Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Thomas Elssner
2009	—	`conf`	Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. Similarity-Based Clustering Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert
2008	—	`ch.`	Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers. Computational Intelligence in Biomedicine and Bioinformatics Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar
2008	J	`jnl`	Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings Bioinform. Thomas Villmann, Frank-Michael Schleif, Markus Kostrzewa, Axel Walch, Barbara Hammer
2008	—	`conf`	Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity. ICONIP (2) Tina Geweniger, Frank-Michael Schleif, Alexander Hasenfuss, Barbara Hammer, Thomas Villmann
2008	J	`jnl`	Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Inteligencia Artif. Marc Strickert, Frank-Michael Schleif, Udo Seiffert, Thomas Villmann
2008	—	`conf`	Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. ANNPR Marc Strickert, Petra Schneider, Jens Keilwagen, Thomas Villmann, Michael Biehl, Barbara Hammer
2008	J	`jnl`	Fuzzy classification using information theoretic learning vector quantization. Neurocomputing Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Wieland Hermann, Marie Cottrell
2008	B	`conf`	Generalized matrix learning vector quantizer for the analysis of spectral data. ESANN Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl
2008	B	`conf`	Machine learning approches and pattern recognition for spectral data. ESANN Thomas Villmann, Erzsébet Merényi, Udo Seiffert
2008	B	`conf`	Magnification Control in Relational Neural Gas. ESANN Alexander Hasenfuss, Barbara Hammer, Tina Geweniger, Thomas Villmann
2008	B	`conf`	Metric adaptation for supervised attribute rating. ESANN Marc Strickert, Frank-Michael Schleif, Thomas Villmann
2008	J	`jnl`	Prototype based fuzzy classification in clinical proteomics. Int. J. Approx. Reason. Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2008	—	`conf`	Robust Centroid-Based Clustering using Derivatives of Pearson Correlation. BIOSIGNALS (2) Marc Strickert, Nese Sreenivasulu, Thomas Villmann, Barbara Hammer
2008	B	`conf`	Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy. CBMS Frank-Michael Schleif, Matthias Ongyerth, Thomas Villmann
2007	—	`conf`	07131 Abstracts Collection -- Similarity-based Clustering and its Application to Medicine and Biology. Similarity-based Clustering and its Application to Medicine and Biology Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann
2007	—	`conf`	07131 Summary -- Similarity-based Clustering and its Application to Medicine and Biology. Similarity-based Clustering and its Application to Medicine and Biology Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann
2007	—	`conf`	Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps. WILF Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2007	C	`conf`	Association Learning in SOMs for Fuzzy-Classification. ICMLA Thomas Villmann, Frank-Michael Schleif, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar
2007	J	`jnl`	Explicit Magnification Control of Self-Organizing Maps for "Forbidden" Data. IEEE Trans. Neural Networks Erzsébet Merényi, Abha Jain, Thomas Villmann
2007	Misc	`conf`	Fuzzy Labeled Self-Organizing Map for Classification of Spectra. IWANN Thomas Villmann, Frank-Michael Schleif, Erzsébet Merényi, Barbara Hammer
2007	B	`conf`	How to process uncertainty in machine learning?. ESANN Barbara Hammer, Thomas Villmann
2007	B	`conf`	Intuitive Clustering of Biological Data. IJCNN Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann, Marc Strickert, Udo Seiffert
2007	J	`jnl`	Magnification control for batch neural gas. Neurocomputing Barbara Hammer, Alexander Hasenfuss, Thomas Villmann
2007	J	`jnl`	Margin-based active learning for LVQ networks. Neurocomputing Frank-Michael Schleif, Barbara Hammer, Thomas Villmann
2007	Misc	`conf`	Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes. IWANN Alexander Hasenfuss, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann
2007	—	`ed.`	Similarity-based Clustering and its Application to Medicine and Biology, 25.03. - 30.03.2007 Similarity-based Clustering and its Application to Medicine and Biology Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann
2007	Misc	`conf`	Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra. IWANN Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2007	B	`conf`	Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. ESANN Thomas Villmann, Marc Strickert, Cornelia Brüß, Frank-Michael Schleif, Udo Seiffert
2006	B	`conf`	Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps. CBMS Frank-Michael Schleif, Thomas Elssner, Markus Kostrzewa, Thomas Villmann, Barbara Hammer
2006	J	`jnl`	Batch and median neural gas. Neural Networks Marie Cottrell, Barbara Hammer, Alexander Hasenfuss, Thomas Villmann
2006	J	`jnl`	Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
2006	J	`jnl`	Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern. Künstliche Intell. Barbara Hammer, Thomas Villmann
2006	—	`conf`	Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. ANNPR Thomas Villmann, Udo Seiffert, Frank-Michael Schleif, Cornelia Brüß, Tina Geweniger, Barbara Hammer
2006	J	`jnl`	Fuzzy classification by fuzzy labeled neural gas. Neural Networks Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Wieland Hermann
2006	B	`conf`	Fuzzy image segmentation with Fuzzy Labelled Neural Gas. ESANN Cornelia Brüß, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert
2006	J	`jnl`	Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis. Neurocomputing Marc Strickert, Udo Seiffert, Nese Sreenivasulu, Winfriede Weschke, Thomas Villmann, Barbara Hammer
2006	Misc	`conf`	Learning Vector Quantization Classification with Local Relevance Determination for Medical Data. ICAISC Barbara Hammer, Thomas Villmann, Frank-Michael Schleif, Cornelia Albani, Wieland Hermann
2006	J	`jnl`	Magnification Control in Self-Organizing Maps and Neural Gas. Neural Comput. Thomas Villmann, Jens Christian Claussen
2006	B	`conf`	Magnification control for batch neural gas. ESANN Barbara Hammer, Alexander Hasenfuss, Thomas Villmann
2006	B	`conf`	Margin based Active Learning for LVQ Networks. ESANN Frank-Michael Schleif, Barbara Hammer, Thomas Villmann
2006	B	`conf`	Neural networks and machine learning in bioinformatics - theory and applications. ESANN Udo Seiffert, Barbara Hammer, Samuel Kaski, Thomas Villmann
2006	—	`conf`	Perspectives of Self-adapted Self-organizing Clustering in Organic Computing. BioADIT Thomas Villmann, Barbara Hammer, Udo Seiffert
2006	—	`conf`	Prototype Based Classification Using Information Theoretic Learning. ICONIP (2) Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Tom Fischer, Marie Cottrell
2006	J	`jnl`	Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
2006	—	`conf`	Supervised Batch Neural Gas. ANNPR Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann
2005	B	`conf`	Classification using non-standard metrics. ESANN Barbara Hammer, Thomas Villmann
2005	C	`conf`	Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. ICMLA Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
2005	B	`conf`	Generalized Relevance LVQ with Correlation Measures for Biological Data. ESANN Marc Strickert, Nese Sreenivasulu, Winfriede Weschke, Udo Seiffert, Thomas Villmann
2005	—	`conf`	Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. WILF Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2005	J	`jnl`	Magnification control in winner relaxing neural gas. Neurocomputing Jens Christian Claussen, Thomas Villmann
2005	J	`jnl`	New Aspects in Neurocomputing. Neurocomputing Marie Cottrell, Barbara Hammer, Thomas Villmann
2005	J	`jnl`	On the Generalization Ability of GRLVQ Networks. Neural Process. Lett. Barbara Hammer, Marc Strickert, Thomas Villmann
2005	B	`conf`	Relevance learning for mental disease classification. ESANN Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann
2005	J	`jnl`	Supervised Neural Gas with General Similarity Measure. Neural Process. Lett. Barbara Hammer, Marc Strickert, Thomas Villmann
2005	J	`jnl`	Trends in Neurocomputing at ESANN 2004. Neurocomputing Jochen J. Steil, Gavin C. Cawley, Thomas Villmann
2004	J	`jnl`	Evolutionary algorithms with neighborhood cooperativeness according to neural maps. Neurocomputing Thomas Villmann, Beate Villmann, Volker Slowik
2004	Misc	`conf`	Relevance LVQ versus SVM. ICAISC Barbara Hammer, Marc Strickert, Thomas Villmann
2004	J	`jnl`	Special issue on new aspects in neurocomputing. Neurocomputing Thomas Villmann
2004	C	`conf`	Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data. ICMLA Frank-Michael Schleif, U. Clauss, Thomas Villmann, Barbara Hammer
2004	B	`conf`	Theory and applications of neural maps. ESANN Thomas Villmann, Udo Seiffert, Axel Wismüller
2003	B	`conf`	Magnification Control in Winner Relaxing Neural Gas. ESANN Jens Christian Claussen, Thomas Villmann
2003	B	`conf`	Mathematical Aspects of Neural Networks. ESANN Barbara Hammer, Thomas Villmann
2003	J	`jnl`	Neural maps in remote sensing image analysis. Neural Networks Thomas Villmann, Erzsébet Merényi, Barbara Hammer
2002	B	`conf`	Batch-RLVQ. ESANN Barbara Hammer, Thomas Villmann
2002	A	`conf`	Evolution Strategy with Neighborhood Attraction Using a Neural Gas Approach. PPSN Jutta Huhse, Thomas Villmann, Peter Merz, Andreas Zell
2002	J	`jnl`	Evolutionary algorithms using a neural network like migration scheme. Integr. Comput. Aided Eng. Thomas Villmann
2002	B	`conf`	Exploratory Data Analysis in Medicine and Bioinformatics. ESANN Axel Wismüller, Thomas Villmann
2002	J	`jnl`	Generalized relevance learning vector quantization. Neural Networks Barbara Hammer, Thomas Villmann
2002	C	`conf`	Learning Vector Quantization for Multimodal Data. ICANN Barbara Hammer, Marc Strickert, Thomas Villmann
2002	J	`jnl`	Neural maps for faithful data modelling in medicine - state-of-the-art and exemplary applications. Neurocomputing Thomas Villmann
2002	C	`conf`	Rule Extraction from Self-Organizing Networks. ICANN Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann
2001	—	`conf`	Clustering of Categoric Data in Medicine - Application of Evolutionary Algorithms. Fuzzy Days Thomas Villmann, Conny Albani
2001	—	`conf`	Estimating Relevant Input Dimensions for Self-organizing Algorithms. WSOM Barbara Hammer, Thomas Villmann
2001	B	`conf`	Evolutionary algorithms and neural networks in hybrid systems. ESANN Thomas Villmann
2001	B	`conf`	Input pruning for neural gas architectures. ESANN Barbara Hammer, Thomas Villmann
2000	—	`conf`	Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Maps. ISMDA Thomas Villmann, Wieland Hermann, Michael Geyer
2000	—	`conf`	Monitoring of Physiological Parameters of Patients and Therapists During Psychotherapy Sessions Using Self-Organizing Maps. ANNIMAB Thomas Villmann, Bernhard Badel, Daniel Kämpf, Michael Geyer
2000	B	`conf`	Neural networks approaches in medicine - a review of actual developments. ESANN Thomas Villmann
2000	—	`conf`	Parallel Evolutionary Algorithms with SOM-Like Migration and its Application to VLSI-Design. IJCNN (5) Thomas Villmann, Reiner Haupt, Klaus Hering
1999	B	`conf`	Benefits and limits of the self-organizing map and its variants in the area of satellite remote sensoring processing. ESANN Thomas Villmann
1999	J	`jnl`	Neural maps and topographic vector quantization. Neural Networks Hans-Ulrich Bauer, J. Michael Herrmann, Thomas Villmann
1999	—	`conf`	Parallel Evolutionary Algorithms with SOM-Like Migration and their Application to Real World Data Sets. ICANNGA Thomas Villmann, Reiner Haupt, Klaus Hering, Hendrik Schulze
1998	J	`jnl`	Applications of the growing self-organizing map. Neurocomputing Thomas Villmann, Hans-Ulrich Bauer
1998	—	`conf`	Evolutionary Algorithms with Self-Organizing Population Dynamic for Clustering of Categories in Psychotherapy Research Using Large Clinical Data Sets. NC Thomas Villmann, A. Körner, Conny Albani
1998	B	`conf`	Magnification control in neural maps. ESANN Thomas Villmann, J. Michael Herrmann
1997	—	`conf`	Application of Evolutionary Algorithms to the Problem of New Clustering of Psychological Categories Using Real Clinical Data Sets. Fuzzy Days Thomas Villmann, Beate Villmann, Conny Albani
1997	J	`jnl`	Growing a hypercubical output space in a self-organizing feature map. IEEE Trans. Neural Networks Hans-Ulrich Bauer, Thomas Villmann
1997	B	`conf`	Measuring topology preservation in maps of real-world data. ESANN J. Michael Herrmann, Hans-Ulrich Bauer, Thomas Villmann
1997	J	`jnl`	Time behavior of topological ordering in self-organizing feature mapping. Biol. Cybern. Ralf Der, J. Michael Herrmann, Thomas Villmann
1997	J	`jnl`	Topology preservation in self-organizing feature maps: exact definition and measurement. IEEE Trans. Neural Networks Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz
1997	C	`conf`	Vector Quantization by Optimal Neural Gas. ICANN J. Michael Herrmann, Thomas Villmann
1996	—	`conf`	Hierarchical Strategy of Model Partitioning for VLSI-Design Using an Improved Mixture of Experts Approach. Workshop on Parallel and Distributed Simulation Klaus Hering, Reiner Haupt, Thomas Villmann
1996	—	`—`	Topologieerhaltung in selbstorganisierenden neuronalen Merkmalskarten. Thomas Villmann
1994	—	`conf`	Topology Preservation in Self-Organizing Feature Maps: General Definition and Efficient Measurement. Fuzzy Days Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz
1993	Misc	`conf`	Dynamics of Self-Organized Feature Mapping. IWANN Ralf Der, Thomas Villmann