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2020 – today
- 2023
[j67]Alexander Engelsberger
, Thomas Villmann:
Quantum Computing Approaches for Vector Quantization - Current Perspectives and Developments. Entropy 25(3): 540 (2023)- [j66]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) - [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) - [c180]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 - 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, Lee John, 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]