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Thomas Villmann
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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]