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ORCIDThomas Villmann
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- affiliation: University of Applied Sciences Mittweida, Germany
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2020 – today
- 2024
[j69]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:
Subspace corrected relevance learning with application in neuroimaging. Artif. Intell. Medicine 149: 102786 (2024)- [c191]Julius Voigt, Sascha Saralajew, Marika Kaden, Katrin Sophie Bohnsack, Lynn V. Reuss, Thomas Villmann:
Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge. BIOSTEC (1) 2024: 357-367 - 2023
- [j68]Alexander Engelsberger, Thomas Villmann:
Quantum Computing Approaches for Vector Quantization - Current Perspectives and Developments. Entropy 25(3): 540 (2023) - [j67]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: 25-39 (2023) - [j66]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: 126632 (2023) - [j65]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): 119-135 (2023) - [c190]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: 59-69 - [c189]Mehrdad Mohannazadeh Bakhtiari, Daniel Staps, Thomas Villmann:
Learning Vector Quantization in Context of Information Bottleneck Theory. ESANN 2023 - [c188]Alexander Engelsberger, Thomas Villmann:
Quantum-ready vector quantization: Prototype learning as a binary optimization problem. ESANN 2023 - [c187]José D. Martín-Guerrero, Lucas Lamata, Thomas Villmann:
Quantum Artificial Intelligence: A tutorial. ESANN 2023 - [c186]Maximilian Münch, Katrin Sophie Bohnsack, Alexander Engelsberger, Frank-Michael Schleif, Thomas Villmann:
Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection. ESANN 2023 - [c185]Thomas Villmann, Ronny Schubert, Marika Kaden:
Variants of Neural Gas for Regression Learning. ESANN 2023 - [c184]Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann, Thomas Villmann:
The Geometry of Decision Borders Between Affine Space Prototypes for Nearest Prototype Classifiers. ICAISC (1) 2023: 134-144 - [c183]Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann:
An Interpretable Two-Layered Neural Network Structure-Based on Component-Wise Reasoning. ICAISC (1) 2023: 145-156 - [c182]Hanno Stage, Lukas Ewecker, Jacob Langner, Tin Stribor Sohn, Thomas Villmann, Eric Sax:
Reducing Computer Vision Dataset Size via Selective Sampling. ITSC 2023: 1422-1428 - [c181]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: 1-5 - [c180]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: 1-5 - 2022
- [j64]Jensun Ravichandran, Marika Kaden, Thomas Villmann:
Variants of recurrent learning vector quantization. Neurocomputing 502: 27-36 (2022) - [j63]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): 67-78 (2022) - [j62]Thomas Villmann, Alexander Engelsberger, Jensun Ravichandran, Andrea Villmann, Marika Kaden:
Quantum-inspired learning vector quantizers for prototype-based classification. Neural Comput. Appl. 34(1): 79-88 (2022) - [c179]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 - [c178]Thomas Villmann, Jonas S. Almeida, John A. Lee, Susana Vinga:
Tutorial - Machine Learning and Information Theoretic Methods for Molecular Biology and Medicine. ESANN 2022 - [c177]Thomas Villmann, Alexander Engelsberger:
Multilayer Perceptrons with Banach-Like Perceptrons Based on Semi-inner Products - About Approximation Completeness. ICAISC (1) 2022: 154-169 - [c176]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: 379-390 - [c175]Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann:
Classification by Components Including Chow's Reject Option. ICONIP (4) 2022: 586-596 - [c174]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: 354-364 - [c173]Daniel Staps, Ronny Schubert, Marika Kaden, Alexander Lampe, Wieland Hermann, Thomas Villmann:
Prototype-based One-Class-Classification Learning Using Local Representations. IJCNN 2022: 1-8 - 2021
- [j61]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): 5507-5513 (2021) - [j60]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): 1357 (2021) - [j59]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): 4405 (2021) - [c172]Marika Kaden, Ronny Schubert, Mehrdad Mohannazadeh Bakhtiari, Lucas Schwarz, Thomas Villmann:
The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach. ESANN 2021 - [c171]Paulo Lisboa, Sascha Saralajew, Alfredo Vellido, Thomas Villmann:
The Coming of Age of Interpretable and Explainable Machine Learning Models. ESANN 2021 - [c170]Jensun Ravichandran, Thomas Villmann, Marika Kaden:
RecLVQ: Recurrent Learning Vector Quantization. ESANN 2021 - [c169]Seyedfakhredin Musavishavazi, Marika Kaden, Thomas Villmann:
Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks. ICAISC (1) 2021: 156-167 - [c168]Thomas Villmann, Alexander Engelsberger:
Quantum-Hybrid Neural Vector Quantization - A Mathematical Approach. ICAISC (1) 2021: 246-257 - [c167]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: 942-949 - [c166]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: 1417-1423 - [i11]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 abs/2110.04916 (2021) - 2020
- [j58]Jensun Ravichandran, Marika Kaden, Sascha Saralajew, Thomas Villmann:
Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability. Neurocomputing 403: 121-132 (2020) - [j57]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): 18085-18099 (2020) - [c165]Thomas Villmann, Jensun Ravichandran, Alexander Engelsberger, Andrea Villmann, Marika Kaden:
Quantum-Inspired Learning Vector Quantization for Classification Learning. ESANN 2020: 279-284 - [c164]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: 547-554 - [c163]Sascha Saralajew, Lars Holdijk, Thomas Villmann:
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms. NeurIPS 2020
2010 – 2019
- 2019
- [j56]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): 1:1-1:16 (2019) - [c162]Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann:
Statistical physics of learning and inference. ESANN 2019 - [c161]Jensun Ravichandran, Sascha Saralajew, Thomas Villmann:
DropConnect for Evaluation of Classification Stability in Learning Vector Quantization. ESANN 2019 - [c160]Thomas Villmann, Marika Kaden, Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann:
Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis. ICAISC (2) 2019: 443-454 - [c159]Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann:
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components. NeurIPS 2019: 2788-2799 - [c158]Thomas Villmann, Jensun Ravichandran, Andrea Villmann, David Nebel, Marika Kaden:
Investigation of Activation Functions for Generalized Learning Vector Quantization. WSOM+ 2019: 179-188 - [c157]Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann:
Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks. WSOM+ 2019: 189-199 - [c156]Tina Geweniger, Thomas Villmann:
Variants of Fuzzy Neural Gas. WSOM+ 2019: 261-270 - [c155]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: 324-333 - [i10]Thomas Villmann, John Ravichandran, Andrea Villmann, David Nebel, Marika Kaden:
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison. CoRR abs/1901.05995 (2019) - [i9]Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann:
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks. CoRR abs/1902.00577 (2019) - 2018
- [j55]Thomas Villmann, Marika Kaden, Wieland Hermann, Michael Biehl:
Learning vector quantization classifiers for ROC-optimization. Comput. Stat. 33(3): 1173-1194 (2018) - [c154]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 - [c153]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: 657-667 - [c152]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: 724-735 - [c151]Thomas Villmann, Tina Geweniger:
Multi-class and Cluster Evaluation Measures Based on Rényi and Tsallis Entropies and Mutual Information. ICAISC (1) 2018: 736-749 - [c150]Thomas Villmann:
Learning Vector Quantization Methods for Interpretable Classification Learning and Multilayer Networks. IJCCI 2018: 15-21 - [i8]Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann:
Prototype-based Neural Network Layers: Incorporating Vector Quantization. CoRR abs/1812.01214 (2018) - 2017
- [j54]David Nebel, Marika Kaden, Andrea Villmann, Thomas Villmann:
Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning. Neurocomputing 268: 42-54 (2017) - [j53]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): 65 (2017) - [c149]Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke:
Biomedical data analysis in translational research: integration of expert knowledge and interpretable models. ESANN 2017 - [c148]Mohammad Mohammadi, Michael Biehl, Andrea Villmann, Thomas Villmann:
Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. ICAISC (1) 2017: 131-142 - [c147]Sascha Saralajew, Thomas Villmann:
Transfer learning in classification based on manifolc. models and its relation to tangent metric learning. IJCNN 2017: 1756-1765 - [c146]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: 69-76 - [c145]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: 139-144 - [c144]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: 220-226 - [c143]Tina Geweniger, Thomas Villmann:
Relational and median variants of Possibilistic Fuzzy C-Means. WSOM 2017: 234-240 - 2016
- [j52]Andrea Bohnsack, Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann:
Learning matrix quantization and relevance learning based on Schatten-p-norms. Neurocomputing 192: 104-114 (2016) - [c142]Michael Biehl, Barbara Hammer, Thomas Villmann:
Prototype-based Models for the Supervised Learning of Classification Schemes. Astroinformatics 2016: 129-138 - [c141]Marika Kaden, David Nebel, Thomas Villmann:
Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities. ESANN 2016 - [c140]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: 125-133 - [c139]Sascha Saralajew, David Nebel, Thomas Villmann:
Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning. ICONIP (3) 2016: 362-371 - [c138]Sascha Saralajew, Thomas Villmann:
Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning. IJCNN 2016: 2672-2679 - [c137]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: 269-279 - [c136]David Nebel, Thomas Villmann:
Optimization of Statistical Evaluation Measures for Classification by Median Learning Vector Quantization. WSOM 2016: 281-291 - [c135]Matthias Gay, Marika Kaden, Michael Biehl, Alexander Lampe, Thomas Villmann:
Complex Variants of GLVQ Based on Wirtinger's Calculus. WSOM 2016: 293-303 - [i7]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): 88-110 (2016) - 2015
- [j51]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): 689-700 (2015) - [j50]Thomas Villmann, Sven Haase, Marika Kaden:
Kernelized vector quantization in gradient-descent learning. Neurocomputing 147: 83-95 (2015) - [j49]Mandy Lange, Michael Biehl, Thomas Villmann:
Non-Euclidean principal component analysis by Hebbian learning. Neurocomputing 147: 107-119 (2015) - [j48]David Nebel, Barbara Hammer, Kathleen Frohberg, Thomas Villmann:
Median variants of learning vector quantization for learning of dissimilarity data. Neurocomputing 169: 295-305 (2015) - [j47]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): 2423-2434 (2015) - [c134]Thomas Villmann:
Sophisticated LVQ Classification Models - Beyond Accuracy Optimization. BrainComp 2015: 116-130 - [c133]Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl:
Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. CAIP (2) 2015: 772-782 - [c132]Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann:
Learning matrix quantization and variants of relevance learning. ESANN 2015 - [c131]David Nebel, Thomas Villmann:
Median-LVQ for classification of dissimilarity data based on ROC-optimization. ESANN 2015 - [c130]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: 403-414 - [c129]Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann:
Stationarity of Matrix Relevance LVQ. IJCNN 2015: 1-8 - [p3]Davide Bacciu, Paulo J. G. Lisboa, Alessandro Sperduti, Thomas Villmann:
Probabilistic Modeling in Machine Learning. Handbook of Computational Intelligence 2015: 545-575 - 2014
- [j46]Barbara Hammer, Thomas Villmann:
Special issue on new challenges in neural computation 2012. Neurocomputing 131: 1 (2014) - [j45]Thomas Villmann, Marika Kaden, David Nebel, Martin Riedel:
Lateral enhancement in adaptive metric learning for functional data. Neurocomputing 131: 23-31 (2014) - [c128]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: 71-77 - [c127]Kristin Domaschke, André Roßberg, Thomas Villmann:
Utilization of Chemical Structure Information for Analysis of Spectra Composites. ESANN 2014 - [c126]Marika Kaden, Wieland Hermann, Thomas Villmann:
Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization. ESANN 2014 - [c125]Mandy Lange, Dietlind Zühlke, Olaf Holz, Thomas Villmann:
Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization. ESANN 2014 - [c124]David Nebel, Barbara Hammer, Thomas Villmann:
Supervised Generative Models for Learning Dissimilarity Data. ESANN 2014 - [c123]Frank-Michael Schleif, Peter Tiño, Thomas Villmann:
Recent trends in learning of structured and non-standard data. ESANN 2014 - [c122]Mandy Lange, David Nebel, Thomas Villmann:
Non-euclidean Principal Component Analysis for Matrices by Hebbian Learning. ICAISC (1) 2014: 77-88 - [c121]Frank-Michael Schleif, Thomas Villmann, Xibin Zhu:
High Dimensional Matrix Relevance Learning. ICDM Workshops 2014: 661-667 - [c120]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: 543-552 - [c119]Marika Kaden, Wieland Hermann, Thomas Villmann:
Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment. WSOM 2014: 77-87 - [c118]Tina Geweniger, Frank-Michael Schleif, Thomas Villmann:
Probabilistic Prototype Classification Using t-norms. WSOM 2014: 99-108 - [c117]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: 109-118 - [c116]Barbara Hammer, David Nebel, Martin Riedel, Thomas Villmann:
Generative versus Discriminative Prototype Based Classification. WSOM 2014: 123-132 - [c115]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: 133-143 - [c114]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: 157-166 - [c113]Mandy Lange, David Nebel, Thomas Villmann:
Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization. WSOM 2014: 259-269 - [e4]Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange:
Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014. Advances in Intelligent Systems and Computing 295, Springer 2014, ISBN 978-3-319-07694-2 [contents] - 2013
- [j44]Tina Geweniger, Lydia Fischer, Marika Kaden, Mandy Lange, Thomas Villmann:
Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters. Comput. Intell. Neurosci. 2013: 165248:1-165248:10 (2013) - [j43]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:
Editorial A Successful Change From TNN to TNNLS and a Very Successful Year. IEEE Trans. Neural Networks Learn. Syst. 24(1): 1-7 (2013) - [c112]Michael Biehl, Barbara Hammer, Thomas Villmann:
Distance Measures for Prototype Based Classification. BrainComp 2013: 100-116 - [c111]Marc Strickert, Barbara Hammer, Thomas Villmann, Michael Biehl:
Regularization and improved interpretation of linear data mappings and adaptive distance measures. CIDM 2013: 10-17 - [c110]Tina Geweniger, Marika Kästner, Thomas Villmann:
Border sensitive fuzzy vector quantization in semi-supervised learning. ESANN 2013 - [c109]Marika Kästner, Marc Strickert, Thomas Villmann:
A sparse kernelized matrix learning vector quantization model for human activity recognition. ESANN 2013 - [c108]Mandy Lange, Michael Biehl, Thomas Villmann:
Non-Euclidean independent component analysis and Oja's learning. ESANN 2013 - [c107]Martin Riedel, Fabrice Rossi, Marika Kästner, Thomas Villmann:
Regularization in relevance learning vector quantization using l1-norms. ESANN 2013 - [c106]Thomas Villmann, Marika Kästner, Andreas Backhaus, Udo Seiffert:
Processing Hyperspectral Data in Machine Learning. ESANN 2013 - [c105]David Nebel, Barbara Hammer, Thomas Villmann:
A Median Variant of Generalized Learning Vector Quantization. ICONIP (2) 2013: 19-26 - [c104]Mandy Lange, Marika Kästner, Thomas Villmann:
About analysis and robust classification of searchlight fMRI-data using machine learning classifiers. IJCNN 2013: 1-8 - [c103]Marika Kästner, Martin Riedel, Marc Strickert, Wieland Hermann, Thomas Villmann:
Border-Sensitive Learning in Kernelized Learning Vector Quantization. IWANN (1) 2013: 357-366 - [i6]Martin Riedel, Marika Kästner, Fabrice Rossi, Thomas Villmann:
Regularization in Relevance Learning Vector Quantization Using l one Norms. CoRR abs/1310.5095 (2013) - 2012
- [j42]Kerstin Bunte, Sven Haase, Michael Biehl, Thomas Villmann:
Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. Neurocomputing 90: 23-45 (2012) - [j41]Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann:
Functional relevance learning in generalized learning vector quantization. Neurocomputing 90: 85-95 (2012) - [j40]Kerstin Bunte, Petra Schneider, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann, Michael Biehl:
Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks 26: 159-173 (2012) - [c102]Charles Bouveyron, Barbara Hammer, Thomas Villmann:
Recent developments in clustering algorithms. ESANN 2012 - [c101]Tina Geweniger, Marika Kästner, Mandy Lange, Thomas Villmann:
Modified Conn-Index for the evaluation of fuzzy clusterings. ESANN 2012 - [c100]Marika Kästner, Wieland Hermann, Thomas Villmann:
Integration of Structural Expert Knowledge about Classes for Classification Using the Fuzzy Supervised Neural Gas. ESANN 2012 - [c99]Thomas Villmann, Erzsébet Merényi, William H. Farrand:
Unmixing Hyperspectral Images with Fuzzy Supervised Self-Organizing Maps. ESANN 2012 - [c98]Marika Kästner, Thomas Villmann:
Fuzzy Supervised Self-Organizing Map for Semi-supervised Vector Quantization. ICAISC (1) 2012: 256-265 - [c97]Thomas Villmann, Tina Geweniger, Marika Kästner, Mandy Lange:
Fuzzy Neural Gas for Unsupervised Vector Quantization. ICAISC (1) 2012: 350-358 - [c96]Marika Kästner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann:
Differentiable Kernels in Generalized Matrix Learning Vector Quantization. ICMLA (1) 2012: 132-137 - [c95]Thomas Villmann, Marika Kästner, David Nebel, Martin Riedel:
ICMLA Face Recognition Challenge - Results of the Team Computational Intelligence Mittweida. ICMLA (2) 2012: 592-595 - [c94]Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann:
Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN 2012: 1-8 - [c93]Gabriele Peters, Kerstin Bunte, Marc Strickert, Michael Biehl, Thomas Villmann:
Visualization of processes in self-learning systems. PST 2012: 244-249 - [c92]Michael Biehl, Marika Kästner, Mandy Lange, Thomas Villmann:
Non-Euclidean Principal Component Analysis and Oja's Learning Rule - Theoretical Aspects. WSOM 2012: 23-33 - [c91]Thomas Villmann, Sven Haase, Marika Kästner:
Gradient Based Learning in Vector Quantization Using Differentiable Kernels. WSOM 2012: 193-204 - 2011
- [j39]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider:
Efficient Kernelized Prototype Based Classification. Int. J. Neural Syst. 21(6): 443-457 (2011) - [j38]Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller:
Neighbor embedding XOM for dimension reduction and visualization. Neurocomputing 74(9): 1340-1350 (2011) - [j37]Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Jennifer R. Aduwo, John A. Quinn, Sven Haase, Thomas Villmann, Michael Biehl:
Divergence-based classification in learning vector quantization. Neurocomputing 74(9): 1429-1435 (2011) - [j36]Thomas Villmann, Sven Haase:
Divergence-Based Vector Quantization. Neural Comput. 23(5): 1343-1392 (2011) - [c90]Kerstin Bunte, Frank-Michael Schleif, Sven Haase, Thomas Villmann:
Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. ESANN 2011 - [c89]Tina Geweniger, Marika Kästner, Thomas Villmann:
Optimization of Parametrized Divergences in Fuzzy c-Means. ESANN 2011 - [c88]Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann:
Generalized functional relevance learning vector quantization. ESANN 2011 - [c87]Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann:
Multivariate class labeling in Robust Soft LVQ. ESANN 2011 - [c86]Marc Strickert, Björn Labitzke, Andreas Kolb, Thomas Villmann:
Multispectral image characterization by partial generalized covariance. ESANN 2011 - [c85]Thomas Villmann, José C. Príncipe, Andrzej Cichocki:
Information theory related learning. ESANN 2011 - [c84]Thomas Villmann, Sven Haase:
Magnification in divergence based neural maps. IJCNN 2011: 437-441 - [c83]M. Kästner, Thomas Villmann:
Functional relevance learning in learning vector quantization for hyperspectral data. WHISPERS 2011: 1-4 - [c82]Thomas Villmann, Marika Kästner:
Sparse Functional Relevance Learning in Generalized Learning Vector Quantization. WSOM 2011: 79-89 - [c81]Marika Kästner, Andreas Backhaus, Tina Geweniger, Sven Haase, Udo Seiffert, Thomas Villmann:
Relevance Learning in Unsupervised Vector Quantization Based on Divergences. WSOM 2011: 90-100 - [i5]Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, Thomas Villmann:
Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). Dagstuhl Reports 1(8): 67-95 (2011) - 2010
- [j35]Tina Geweniger, Dietlind Zühlke, Barbara Hammer, Thomas Villmann:
Median fuzzy c-means for clustering dissimilarity data. Neurocomputing 73(7-9): 1109-1116 (2010) - [j34]Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Barbara Hammer:
Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowl. Inf. Syst. 25(2): 327-343 (2010) - [j33]Petra Schneider, Kerstin Bunte, Han Stiekema, Barbara Hammer, Thomas Villmann, Michael Biehl:
Regularization in matrix relevance learning. IEEE Trans. Neural Networks 21(5): 831-840 (2010) - [c80]Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer, Michael Biehl:
The Mathematics of Divergence Based Online Learning in Vector Quantization. ANNPR 2010: 108-119 - [c79]Andreas Schierwagen, Thomas Villmann, Alán Alpár, Ulrich Gärtner:
Cluster Analysis of Cortical Pyramidal Neurons Using SOM. ANNPR 2010: 120-130 - [c78]Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller:
Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. ESANN 2010 - [c77]Tina Geweniger, Thomas Villmann:
Extending FSNPC to handle data points with fuzzy class assignments. ESANN 2010 - [c76]Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl:
Divergence based Learning Vector Quantization. ESANN 2010 - [c75]Thomas Villmann, Frank-Michael Schleif, Barbara Hammer:
Sparse representation of data. ESANN 2010 - [c74]Dietlind Zühlke, Frank-Michael Schleif, Tina Geweniger, Sven Haase, Thomas Villmann:
Learning vector quantization for heterogeneous structured data. ESANN 2010 - [c73]Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer:
Divergence Based Online Learning in Vector Quantization. ICAISC (1) 2010: 479-486 - [c72]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl:
Generalized Derivative Based Kernelized Learning Vector Quantization. IDEAL 2010: 21-28 - [c71]Thomas Villmann, Sven Haase:
Divergence based vector quantization of spectral data. WHISPERS 2010: 1-4
2000 – 2009
- 2009
- [j32]Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa, Barbara Hammer, Alexander Gammerman:
Cancer informatics by prototype networks in mass spectrometry. Artif. Intell. Medicine 45(2-3): 215-228 (2009) - [j31]Frank-Michael Schleif, Thomas Villmann, Matthias Ongyerth:
Supervised data analysis and reliability estimation with exemplary application for spectral data. Neurocomputing 72(16-18): 3590-3601 (2009) - [c70]Thomas Villmann, Barbara Hammer, Michael Biehl:
Some Theoretical Aspects of the Neural Gas Vector Quantizer. Similarity-Based Clustering 2009: 23-34 - [c69]Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert:
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. Similarity-Based Clustering 2009: 70-91 - [c68]Tina Geweniger, Dietlind Zühlke, Barbara Hammer, Thomas Villmann:
Median Variant of Fuzzy c-Means. ESANN 2009 - [c67]Frank-Michael Schleif, Thomas Villmann:
Neural Maps and Learning Vector Quantization - Theory and Applications. ESANN 2009 - [c66]Dietlind Zühlke, Tina Geweniger, Ulrich Heimann, Thomas Villmann:
Fuzzy Fleiss-kappa for Comparison of Fuzzy Classifiers. ESANN 2009 - [c65]Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Thomas Elssner:
Tanimoto Metric in Tree-SOM for Improved Representation of Mass Spectrometry Data with an Underlying Taxonomic Structure. ICMLA 2009: 563-567 - [c64]Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl:
Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. IWANN (1) 2009: 933-940 - [c63]Thomas Villmann, Frank-Michael Schleif:
Funtional vector quantization by neural maps. WHISPERS 2009: 1-4 - [c62]Tina Geweniger, Dietlind Zühlke, Barbara Hammer, Thomas Villmann:
Fuzzy Variant of Affinity Propagation in Comparison to Median Fuzzy c-Means. WSOM 2009: 72-79 - [c61]Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa:
Hierarchical PCA Using Tree-SOM for the Identification of Bacteria. WSOM 2009: 272-280 - [c60]Thomas Villmann, Barbara Hammer:
Functional Principal Component Learning Using Oja's Method and Sobolev Norms. WSOM 2009: 325-333 - [p2]Michael Biehl, Barbara Hammer, Petra Schneider, Thomas Villmann:
Metric Learning for Prototype-Based Classification. Innovations in Neural Information Paradigms and Applications 2009: 183-199 - [e3]Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann:
Similarity-Based Clustering, Recent Developments and Biomedical Applications [outcome of a Dagstuhl Seminar]. Lecture Notes in Computer Science 5400, Springer 2009, ISBN 978-3-642-01804-6 [contents] - [e2]Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann:
Similarity-based learning on structures, 15.02. - 20.02.2009. Dagstuhl Seminar Proceedings 09081, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany 2009 [contents] - [r1]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer:
Prototype Based Classification in Bioinformatics. Encyclopedia of Artificial Intelligence 2009: 1337-1342 - [i4]Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann:
09081 Abstracts Collection - Similarity-based learning on structures. Similarity-based learning on structures 2009 - [i3]Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann:
09081 Summary - Similarity-based learning on structures. Similarity-based learning on structures 2009 - 2008
- [j30]Marc Strickert, Frank-Michael Schleif, Udo Seiffert, Thomas Villmann:
Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Inteligencia Artif. 12(37): 37-44 (2008) - [j29]Thomas Villmann, Frank-Michael Schleif, Markus Kostrzewa, Axel Walch, Barbara Hammer:
Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings Bioinform. 9(2): 129-143 (2008) - [j28]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer:
Prototype based fuzzy classification in clinical proteomics. Int. J. Approx. Reason. 47(1): 4-16 (2008) - [j27]Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Wieland Hermann, Marie Cottrell:
Fuzzy classification using information theoretic learning vector quantization. Neurocomputing 71(16-18): 3070-3076 (2008) - [c59]Marc Strickert, Petra Schneider, Jens Keilwagen, Thomas Villmann, Michael Biehl, Barbara Hammer:
Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. ANNPR 2008: 78-89 - [c58]Marc Strickert, Nese Sreenivasulu, Thomas Villmann, Barbara Hammer:
Robust Centroid-Based Clustering using Derivatives of Pearson Correlation. BIOSIGNALS (2) 2008: 197-203 - [c57]Frank-Michael Schleif, Matthias Ongyerth, Thomas Villmann:
Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy. CBMS 2008: 620-625 - [c56]Marc Strickert, Frank-Michael Schleif, Thomas Villmann:
Metric adaptation for supervised attribute rating. ESANN 2008: 31-36 - [c55]Alexander Hasenfuss, Barbara Hammer, Tina Geweniger, Thomas Villmann:
Magnification Control in Relational Neural Gas. ESANN 2008: 325-330 - [c54]Thomas Villmann, Erzsébet Merényi, Udo Seiffert:
Machine learning approches and pattern recognition for spectral data. ESANN 2008: 433-444 - [c53]Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl:
Generalized matrix learning vector quantizer for the analysis of spectral data. ESANN 2008: 451-456 - [c52]Tina Geweniger, Frank-Michael Schleif, Alexander Hasenfuss, Barbara Hammer, Thomas Villmann:
Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity. ICONIP (2) 2008: 61-69 - [p1]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar:
Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers. Computational Intelligence in Biomedicine and Bioinformatics 2008: 141-167 - 2007
- [j26]Frank-Michael Schleif, Barbara Hammer, Thomas Villmann:
Margin-based active learning for LVQ networks. Neurocomputing 70(7-9): 1215-1224 (2007) - [j25]Barbara Hammer, Alexander Hasenfuss, Thomas Villmann:
Magnification control for batch neural gas. Neurocomputing 70(7-9): 1225-1234 (2007) - [j24]Erzsébet Merényi, Abha Jain, Thomas Villmann:
Explicit Magnification Control of Self-Organizing Maps for "Forbidden" Data. IEEE Trans. Neural Networks 18(3): 786-797 (2007) - [c51]Barbara Hammer, Thomas Villmann:
How to process uncertainty in machine learning?. ESANN 2007: 79-90 - [c50]Thomas Villmann, Marc Strickert, Cornelia Brüß, Frank-Michael Schleif, Udo Seiffert:
Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. ESANN 2007: 103-108 - [c49]Thomas Villmann, Frank-Michael Schleif, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar:
Association Learning in SOMs for Fuzzy-Classification. ICMLA 2007: 581-586 - [c48]Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann, Marc Strickert, Udo Seiffert:
Intuitive Clustering of Biological Data. IJCNN 2007: 1877-1882 - [c47]Alexander Hasenfuss, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann:
Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes. IWANN 2007: 539-546 - [c46]Thomas Villmann, Frank-Michael Schleif, Erzsébet Merényi, Barbara Hammer:
Fuzzy Labeled Self-Organizing Map for Classification of Spectra. IWANN 2007: 556-563 - [c45]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer:
Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra. IWANN 2007: 1036-1044 - [c44]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer:
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps. WILF 2007: 563-570 - [e1]Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann:
Similarity-based Clustering and its Application to Medicine and Biology, 25.03. - 30.03.2007. Dagstuhl Seminar Proceedings 07131, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2007 [contents] - [i2]Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann:
07131 Summary -- Similarity-based Clustering and its Application to Medicine and Biology. Similarity-based Clustering and its Application to Medicine and Biology 2007 - [i1]Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann:
07131 Abstracts Collection -- Similarity-based Clustering and its Application to Medicine and Biology. Similarity-based Clustering and its Application to Medicine and Biology 2007 - 2006
- [j23]Marc Strickert, Udo Seiffert, Nese Sreenivasulu, Winfriede Weschke, Thomas Villmann, Barbara Hammer:
Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis. Neurocomputing 69(7-9): 651-659 (2006) - [j22]Thomas Villmann, Frank-Michael Schleif, Barbara Hammer:
Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing 69(16-18): 2425-2428 (2006) - [j21]Barbara Hammer, Thomas Villmann:
Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern. Künstliche Intell. 20(3): 5-11 (2006) - [j20]Thomas Villmann, Jens Christian Claussen:
Magnification Control in Self-Organizing Maps and Neural Gas. Neural Comput. 18(2): 446-469 (2006) - [j19]Thomas Villmann, Frank-Michael Schleif, Barbara Hammer:
Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks 19(5): 610-622 (2006) - [j18]Marie Cottrell, Barbara Hammer, Alexander Hasenfuss, Thomas Villmann:
Batch and median neural gas. Neural Networks 19(6-7): 762-771 (2006) - [j17]Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Wieland Hermann:
Fuzzy classification by fuzzy labeled neural gas. Neural Networks 19(6-7): 772-779 (2006) - [c43]Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann:
Supervised Batch Neural Gas. ANNPR 2006: 33-45 - [c42]Thomas Villmann, Udo Seiffert, Frank-Michael Schleif, Cornelia Brüß, Tina Geweniger, Barbara Hammer:
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. ANNPR 2006: 46-56 - [c41]Thomas Villmann, Barbara Hammer, Udo Seiffert:
Perspectives of Self-adapted Self-organizing Clustering in Organic Computing. BioADIT 2006: 141-159 - [c40]Frank-Michael Schleif, Thomas Elssner, Markus Kostrzewa, Thomas Villmann, Barbara Hammer:
Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps. CBMS 2006: 919-924 - [c39]Barbara Hammer, Alexander Hasenfuss, Thomas Villmann:
Magnification control for batch neural gas. ESANN 2006: 7-12 - [c38]Udo Seiffert, Barbara Hammer, Samuel Kaski, Thomas Villmann:
Neural networks and machine learning in bioinformatics - theory and applications. ESANN 2006: 521-532 - [c37]Frank-Michael Schleif, Barbara Hammer, Thomas Villmann:
Margin based Active Learning for LVQ Networks. ESANN 2006: 539-544 - [c36]Cornelia Brüß, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert:
Fuzzy image segmentation with Fuzzy Labelled Neural Gas. ESANN 2006: 563-568 - [c35]Barbara Hammer, Thomas Villmann, Frank-Michael Schleif, Cornelia Albani, Wieland Hermann:
Learning Vector Quantization Classification with Local Relevance Determination for Medical Data. ICAISC 2006: 603-612 - [c34]Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Tom Fischer, Marie Cottrell:
Prototype Based Classification Using Information Theoretic Learning. ICONIP (2) 2006: 40-49 - 2005
- [j16]Marie Cottrell, Barbara Hammer, Thomas Villmann:
New Aspects in Neurocomputing. Neurocomputing 63: 1-3 (2005) - [j15]Jens Christian Claussen, Thomas Villmann:
Magnification control in winner relaxing neural gas. Neurocomputing 63: 125-137 (2005) - [j14]Jochen J. Steil, Gavin C. Cawley, Thomas Villmann:
Trends in Neurocomputing at ESANN 2004. Neurocomputing 64: 1-4 (2005) - [j13]Barbara Hammer, Marc Strickert, Thomas Villmann:
Supervised Neural Gas with General Similarity Measure. Neural Process. Lett. 21(1): 21-44 (2005) - [j12]Barbara Hammer, Marc Strickert, Thomas Villmann:
On the Generalization Ability of GRLVQ Networks. Neural Process. Lett. 21(2): 109-120 (2005) - [c33]Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann:
Relevance learning for mental disease classification. ESANN 2005: 139-144 - [c32]Barbara Hammer, Thomas Villmann:
Classification using non-standard metrics. ESANN 2005: 303-316 - [c31]Marc Strickert, Nese Sreenivasulu, Winfriede Weschke, Udo Seiffert, Thomas Villmann:
Generalized Relevance LVQ with Correlation Measures for Biological Data. ESANN 2005: 331-338 - [c30]Thomas Villmann, Frank-Michael Schleif, Barbara Hammer:
Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. ICMLA 2005 - [c29]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer:
Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. WILF 2005: 290-296 - 2004
- [j11]Thomas Villmann:
Special issue on new aspects in neurocomputing. Neurocomputing 57: 1-2 (2004) - [j10]Thomas Villmann, Beate Villmann, Volker Slowik:
Evolutionary algorithms with neighborhood cooperativeness according to neural maps. Neurocomputing 57: 151-169 (2004) - [c28]Thomas Villmann, Udo Seiffert, Axel Wismüller:
Theory and applications of neural maps. ESANN 2004: 25-38 - [c27]Barbara Hammer, Marc Strickert, Thomas Villmann:
Relevance LVQ versus SVM. ICAISC 2004: 592-597 - [c26]Frank-Michael Schleif, U. Clauss, Thomas Villmann, Barbara Hammer:
Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data. ICMLA 2004: 374-379 - 2003
- [j9]Thomas Villmann, Erzsébet Merényi, Barbara Hammer:
Neural maps in remote sensing image analysis. Neural Networks 16(3-4): 389-403 (2003) - [c25]Barbara Hammer, Thomas Villmann:
Mathematical Aspects of Neural Networks. ESANN 2003: 59-72 - [c24]Jens Christian Claussen, Thomas Villmann:
Magnification Control in Winner Relaxing Neural Gas. ESANN 2003: 93-98 - 2002
- [j8]Thomas Villmann:
Evolutionary algorithms using a neural network like migration scheme. Integr. Comput. Aided Eng. 9(1): 25-35 (2002) - [j7]Thomas Villmann:
Neural maps for faithful data modelling in medicine - state-of-the-art and exemplary applications. Neurocomputing 48(1-4): 229-250 (2002) - [j6]Barbara Hammer, Thomas Villmann:
Generalized relevance learning vector quantization. Neural Networks 15(8-9): 1059-1068 (2002) - [c23]Axel Wismüller, Thomas Villmann:
Exploratory Data Analysis in Medicine and Bioinformatics. ESANN 2002: 25-38 - [c22]Barbara Hammer, Thomas Villmann:
Batch-RLVQ. ESANN 2002: 295-300 - [c21]Barbara Hammer, Marc Strickert, Thomas Villmann:
Learning Vector Quantization for Multimodal Data. ICANN 2002: 370-376 - [c20]Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann:
Rule Extraction from Self-Organizing Networks. ICANN 2002: 877-883 - [c19]Jutta Huhse, Thomas Villmann, Peter Merz, Andreas Zell:
Evolution Strategy with Neighborhood Attraction Using a Neural Gas Approach. PPSN 2002: 391-400 - 2001
- [c18]Thomas Villmann:
Evolutionary algorithms and neural networks in hybrid systems. ESANN 2001: 137-152 - [c17]Barbara Hammer, Thomas Villmann:
Input pruning for neural gas architectures. ESANN 2001: 283-288 - [c16]Thomas Villmann, Conny Albani:
Clustering of Categoric Data in Medicine - Application of Evolutionary Algorithms. Fuzzy Days 2001: 619-627 - [c15]Barbara Hammer, Thomas Villmann:
Estimating Relevant Input Dimensions for Self-organizing Algorithms. WSOM 2001: 173-180 - 2000
- [c14]Thomas Villmann, Bernhard Badel, Daniel Kämpf, Michael Geyer:
Monitoring of Physiological Parameters of Patients and Therapists During Psychotherapy Sessions Using Self-Organizing Maps. ANNIMAB 2000: 221-226 - [c13]Thomas Villmann:
Neural networks approaches in medicine - a review of actual developments. ESANN 2000: 165-176 - [c12]Thomas Villmann, Reiner Haupt, Klaus Hering:
Parallel Evolutionary Algorithms with SOM-Like Migration and its Application to VLSI-Design. IJCNN (5) 2000: 167-172 - [c11]Thomas Villmann, Wieland Hermann, Michael Geyer:
Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Maps. ISMDA 2000: 138-151
1990 – 1999
- 1999
- [j5]Hans-Ulrich Bauer, J. Michael Herrmann, Thomas Villmann:
Neural maps and topographic vector quantization. Neural Networks 12(4-5): 659-676 (1999) - [c10]Thomas Villmann:
Benefits and limits of the self-organizing map and its variants in the area of satellite remote sensoring processing. ESANN 1999: 111-116 - [c9]Thomas Villmann, Reiner Haupt, Klaus Hering, Hendrik Schulze:
Parallel Evolutionary Algorithms with SOM-Like Migration and their Application to Real World Data Sets. ICANNGA 1999: 274-279 - 1998
- [j4]Thomas Villmann, Hans-Ulrich Bauer:
Applications of the growing self-organizing map. Neurocomputing 21(1-3): 91-100 (1998) - [c8]Thomas Villmann, J. Michael Herrmann:
Magnification control in neural maps. ESANN 1998: 191-196 - [c7]Thomas Villmann, A. Körner, Conny Albani:
Evolutionary Algorithms with Self-Organizing Population Dynamic for Clustering of Categories in Psychotherapy Research Using Large Clinical Data Sets. NC 1998: 130-136 - 1997
- [j3]Ralf Der, J. Michael Herrmann, Thomas Villmann:
Time behavior of topological ordering in self-organizing feature mapping. Biol. Cybern. 77(6): 419-427 (1997) - [j2]Hans-Ulrich Bauer, Thomas Villmann:
Growing a hypercubical output space in a self-organizing feature map. IEEE Trans. Neural Networks 8(2): 218-226 (1997) - [j1]Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz:
Topology preservation in self-organizing feature maps: exact definition and measurement. IEEE Trans. Neural Networks 8(2): 256-266 (1997) - [c6]J. Michael Herrmann, Hans-Ulrich Bauer, Thomas Villmann:
Measuring topology preservation in maps of real-world data. ESANN 1997 - [c5]Thomas Villmann, Beate Villmann, Conny Albani:
Application of Evolutionary Algorithms to the Problem of New Clustering of Psychological Categories Using Real Clinical Data Sets. Fuzzy Days 1997: 311-320 - [c4]J. Michael Herrmann, Thomas Villmann:
Vector Quantization by Optimal Neural Gas. ICANN 1997: 625-630 - 1996
- [b1]Thomas Villmann:
Topologieerhaltung in selbstorganisierenden neuronalen Merkmalskarten. Leipzig University, Germany, Deutsch 1996, ISBN 3-8171-1523-7, pp. 1-116 - [c3]Klaus Hering, Reiner Haupt, Thomas Villmann:
Hierarchical Strategy of Model Partitioning for VLSI-Design Using an Improved Mixture of Experts Approach. Workshop on Parallel and Distributed Simulation 1996: 106-113 - 1994
- [c2]Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz:
Topology Preservation in Self-Organizing Feature Maps: General Definition and Efficient Measurement. Fuzzy Days 1994: 159-166 - 1993
- [c1]Ralf Der, Thomas Villmann:
Dynamics of Self-Organized Feature Mapping. IWANN 1993: 312-315
Coauthor Index
[c142] [c137] [j48] [c129] [j46] [c124] [c117] [c116] [c112] [c111] [c105] [j41] [j40] [c102] [j39] [j38] [c88] [i5] [j35] [j34] [j33] [c80] [c78] [c75] [c73] [c72] [j32] [c70] [c68] [c62] [c60] [p2] [e3] [e2] [r1] [i4] [i3] [j29] [j28] [j27] [c59] [c58] [c55] [c52] [p1] [j26] [j25] [c51] [c48] [c47] [c46] [c45] [c44] [e1] [i2] [i1] [j23] [j22] [j21] [j19] [j18] [j17] [c43] [c42] [c41] [c40] [c39] [c38] [c37] [c35] [c34] [j16] [j13] [j12] [c33] [c32] [c30] [c29] [c27] [c26] [j9] [c25] [j6] [c22] [c21] [c20] [c17] [c15]
Marika Kaden
aka: Marika Kästner
aka: Marika Kästner
[c191] [j66] [j65] [c190] [c185] [c181] [c180] [j64] [j63] [j62] [c179] [c176] [c174] [c173] [j60] [j59] [c172] [c170] [c169] [c166] [j58] [j57] [c165] [j56] [c160] [c158] [c155] [i10] [j55] [c154] [c152] [j54] [j53] [c145] [c144] [j52] [c141] [c140] [c137] [c135] [j50] [j47] [c133] [c132] [c130] [j45] [c128] [c126] [c119] [c114] [e4] [j44] [c110] [c109] [c107] [c106] [c104] [c103] [i6] [j41] [c101] [c100] [c98] [c97] [c96] [c95] [c92] [c91] [c89] [c88] [c82] [c81]
[c186] [c129] [c123] [c121] [c118] [e4] [j40] [c94] [j39] [j37] [c90] [c87] [j34] [c80] [c76] [c75] [c74] [c73] [c72] [j32] [j31] [c69] [c67] [c65] [c64] [c63] [c61] [r1] [j30] [j29] [j28] [j27] [c57] [c56] [c53] [c52] [p1] [j26] [c50] [c49] [c48] [c47] [c46] [c45] [c44] [j22] [j19] [j17] [c43] [c42] [c40] [c37] [c36] [c35] [c34] [c30] [c29] [c26]
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last updated on 2024-08-05 21:18 CEST by the dblp team
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