| 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 | 59 | 2014 |
| Can Learning Vector Quantization be an Alternative to SVM and Deep Learning?-Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning T Villmann, A Bohnsack, M Kaden Journal of Artificial Intelligence and Soft Computing Research 7 (1), 65-81, 2017 | 48 | 2017 |
| Functional relevance learning in generalized learning vector quantization M Kästner, B Hammer, M Biehl, T Villmann Neurocomputing 90, 85-95, 2012 | 47 | 2012 |
| Kernelized vector quantization in gradient-descent learning T Villmann, S Haase, M Kaden Neurocomputing 147, 83-95, 2015 | 41 | 2015 |
| A sparse kernelized matrix learning vector quantization model for human activity recognition. M Kästner, M Strickert, T Villmann, SG Mittweida ESANN, 2013 | 31 | 2013 |
| Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines M Kaden, M Riedel, W Hermann, T Villmann Soft Computing, 1-12, 2014 | 26 | 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 | 22 | 2017 |
| Self-Adjusting Reject Options in Prototype Based Classification T Villmann, M Kaden, A Bohnsack, JM Villmann, T Drogies, S Saralajew, ... Advances in Self-Organizing Maps and Learning Vector Quantization, 269-279, 2016 | 16 | 2016 |
| Gradient based learning in vector quantization using differentiable kernels T Villmann, S Haase, M Kästner Advances in Self-Organizing Maps, 193-204, 2013 | 16 | 2013 |
| Learning vector quantization classifiers for ROC-optimization T Villmann, M Kaden, W Hermann, M Biehl Computational Statistics 33 (3), 1173-1194, 2018 | 14 | 2018 |
| Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization. M Kaden, W Hermann, T Villmann ESANN, 2014 | 14 | 2014 |
| Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014 T Villmann, FM Schleif, M Kaden, M Lange Springer, 2014 | 13* | 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 | 13 | 2012 |
| Generalized functional relevance learning vector quantization M Kästner, B Hammer, M Biehl, T Villmann Proceedings of the 19. European Symposium on Artificial Neural Networks …, 2011 | 13 | 2011 |
| Fuzzy supervised self-organizing map for semi-supervised vector quantization M Kästner, T Villmann Artificial Intelligence and Soft Computing, 256-265, 2012 | 12 | 2012 |
| Learning Vector Quantization Methods for Interpretable Classification Learning and Multilayer Networks. T Villmann, S Saralajew, A Villmann, M Kaden IJCCI, 15-21, 2018 | 11 | 2018 |
| Precision-Recall-Optimization in Learning vector quantization classifiers for improved medical classification systems T Villmann, M Kaden, M Lange, P Stürmer, W Hermann 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 71-77, 2014 | 11 | 2014 |
| Investigation of Activation Functions for Generalized Learning Vector Quantization T Villmann, J Ravichandran, A Villmann, D Nebel, M Kaden International Workshop on Self-Organizing Maps, 179-188, 2019 | 10 | 2019 |
| Border-sensitive learning in kernelized learning vector quantization M Kästner, M Riedel, M Strickert, W Hermann, T Villmann International Work-Conference on Artificial Neural Networks, 357-366, 2013 | 10 | 2013 |
| Non-Euclidean principal component analysis and Oja’s learning rule–theoretical aspects M Biehl, M Kästner, M Lange, T Villmann Advances in Self-Organizing Maps, 23-33, 2013 | 10 | 2013 |
Thomas VillmannProfessor for Mathematics and Computational Intelligence, University of Applied Sciences MittweidaBestätigte E-Mail-Adresse bei hs-mittweida.de
