| 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 | 81 | 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, 2017 | 75 | 2017 |
| Kernelized vector quantization in gradient-descent learning T Villmann, S Haase, M Kaden Neurocomputing 147, 83-95, 2015 | 52 | 2015 |
| Functional relevance learning in generalized learning vector quantization M Kästner, B Hammer, M Biehl, T Villmann Neurocomputing 90, 85-95, 2012 | 51 | 2012 |
| 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 | 43 | 2017 |
| A sparse kernelized matrix learning vector quantization model for human activity recognition. M Kästner, M Strickert, T Villmann, SG Mittweida ESANN, 2013 | 42 | 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 | 32 | 2014 |
| Alignment-free sequence comparison: A systematic survey from a machine learning perspective KS Bohnsack, M Kaden, J Abel, T Villmann IEEE/ACM Transactions on Computational Biology and Bioinformatics 20 (1 …, 2022 | 30 | 2022 |
| Application of an interpretable classification model on Early Folding Residues during protein folding S Bittrich, M Kaden, C Leberecht, F Kaiser, T Villmann, D Labudde BioData mining 12 (1), 1-16, 2019 | 27 | 2019 |
| 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 …, 2016 | 27 | 2016 |
| Quantum-inspired learning vector quantizers for prototype-based classification T Villmann, A Engelsberger, J Ravichandran, A Villmann, M Kaden Neural Computing and Applications, 1-10, 2020 | 23 | 2020 |
| 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 | 22 | 2020 |
| Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization. M Kaden, W Hermann, T Villmann ESANN, 2014 | 21 | 2014 |
| Gradient based learning in vector quantization using differentiable kernels T Villmann, S Haase, M Kästner Advances in Self-Organizing Maps: 9th International Workshop, WSOM 2012 …, 2013 | 20 | 2013 |
| Generalized functional relevance learning vector quantization M Kästner, B Hammer, M Biehl, T Villmann 19th European Symposium on Artificial Neural Networks (ESANN 2011), 93-98, 2011 | 20 | 2011 |
| 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 | 19 | 2014 |
| Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences M Kaden, KS Bohnsack, M Weber, M Kudła, K Gutowska, J Blazewicz, ... Neural Computing and Applications 34 (1), 67-78, 2022 | 18 | 2022 |
| Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability J Ravichandran, M Kaden, S Saralajew, T Villmann Neurocomputing 403, 121-132, 2020 | 18 | 2020 |
| Probabilistic learning vector quantization with cross-entropy for probabilistic class assignments in classification learning A Villmann, M Kaden, S Saralajew, T Villmann Artificial Intelligence and Soft Computing: 17th International Conference …, 2018 | 18 | 2018 |
| 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 | 18* | 2014 |
Thomas VillmannProfessor for Mathematics and Computational Intelligence, University of Applied Sciences MittweidaVerified email at hs-mittweida.de
