| Aspects in classification learning-Review of recent developments in Learning Vector Quantization M Kaden, M Lange, D Nebel, M Riedel, T Geweniger, T Villmann Foundations of Computing and Decision Sciences 39 (2), 79-105, 2014 | 71 | 2014 |
| Types of (dis-) similarities and adaptive mixtures thereof for improved classification learning D Nebel, M Kaden, A Villmann, T Villmann Neurocomputing 268, 42-54, 2017 | 30 | 2017 |
| Median variants of learning vector quantization for learning of dissimilarity data D Nebel, B Hammer, K Frohberg, T Villmann Neurocomputing 169, 295-305, 2015 | 28 | 2015 |
| About learning of supervised generative models for dissimilarity data D Nebel, B Hammer, T Villmann Machine Learning Reports 7, 1-19, 2013 | 23 | 2013 |
| Investigation of activation functions for generalized learning vector quantization T Villmann, J Ravichandran, A Villmann, D Nebel, M Kaden Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering …, 2020 | 20 | 2020 |
| Generative versus discriminative prototype based classification B Hammer, D Nebel, M Riedel, T Villmann Advances in Self-Organizing Maps and Learning Vector Quantization …, 2014 | 20 | 2014 |
| A median variant of generalized learning vector quantization D Nebel, B Hammer, T Villmann Neural Information Processing: 20th International Conference, ICONIP 2013 …, 2013 | 19 | 2013 |
| Rejection strategies for learning vector quantization–a comparison of probabilistic and deterministic approaches L Fischer, D Nebel, T Villmann, B Hammer, H Wersing Advances in Self-Organizing Maps and Learning Vector Quantization …, 2014 | 17 | 2014 |
| Differentiable kernels in generalized matrix learning vector quantization M Kästner, D Nebel, M Riedel, M Biehl, T Villmann 2012 11th International Conference on Machine Learning and Applications 1 …, 2012 | 16 | 2012 |
| Learning vector quantization with adaptive cost-based outlier-rejection T Villmann, M Kaden, D Nebel, M Biehl Computer Analysis of Images and Patterns: 16th International Conference …, 2015 | 14 | 2015 |
| Building the library of RNA 3D nucleotide conformations using the clustering approach T Zok, M Antczak, M Riedel, D Nebel, T Villmann, P Lukasiak, J Blazewicz, ... International Journal of Applied Mathematics and Computer Science 25 (3 …, 2015 | 10 | 2015 |
| Supervised Generative Models for Learning Dissimilarity Data. D Nebel, B Hammer, T Villmann ESANN, 2014 | 10 | 2014 |
| Adaptive Hausdorff distances and tangent distance adaptation for transformation invariant classification learning S Saralajew, D Nebel, T Villmann Neural Information Processing: 23rd International Conference, ICONIP 2016 …, 2016 | 9 | 2016 |
| ICMLA Face Recognition Challenge--Results of the Team Computational Intelligence Mittweida T Villmann, M Kästner, D Nebel, M Riedel 2012 11th International Conference on Machine Learning and Applications 2 …, 2012 | 8 | 2012 |
| Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning: A Mathematical Characterization T Villmann, M Kaden, D Nebel, A Bohnsack Artificial Intelligence and Soft Computing: 15th International Conference …, 2016 | 7 | 2016 |
| Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities. M Kaden, D Nebel, T Villmann, M Verleysen ESANN, 2016 | 6 | 2016 |
| Activation functions for generalized learning vector quantization-a performance comparison T Villmann, J Ravichandran, A Villmann, D Nebel, M Kaden arXiv preprint arXiv:1901.05995, 2019 | 4 | 2019 |
| Lateral enhancement in adaptive metric learning for functional data T Villmann, M Kaden, D Nebel, M Riedel Neurocomputing 131, 23-31, 2014 | 4 | 2014 |
| Median variants of LVQ for optimization of statistical quality measures for classification of dissimilarity data D Nebel, T Villmann Machine Learning Reports 8 (MLR-03-2014), 1-25, 2014 | 3 | 2014 |
| Partial mutual information for classification of gene expression data by learning vector quantization M Lange, D Nebel, T Villmann Advances in Self-Organizing Maps and Learning Vector Quantization …, 2014 | 3 | 2014 |
Thomas VillmannProfessor for Mathematics and Computational Intelligence, University of Applied Sciences MittweidaGeverifieerd e-mailadres voor hs-mittweida.de
