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Thomas 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
aka: Marika Kästner
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