| Generalized relevance learning vector quantization B Hammer, T Villmann Neural Networks 15 (8-9), 1059-1068, 2002 | 595 | 2002 |
| Incremental learning algorithms and applications A Geppert, B Hammer ESANN, 2016 | 514 | 2016 |
| Adaptive relevance matrices in learning vector quantization P Schneider, M Biehl, B Hammer Neural computation 21 (12), 3532-3561, 2009 | 498 | 2009 |
| Incremental on-line learning: A review and comparison of state of the art algorithms V Losing, B Hammer, H Wersing Neurocomputing 275, 1261-1274, 2018 | 469 | 2018 |
| KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift V Losing, B Hammer, H Wersing IEEE International Conference on Data Mining (ICDM), 2016 | 344 | 2016 |
| Parametric nonlinear dimensionality reduction using kernel t-SNE A Gisbrecht, A Schulz, B Hammer Neurocomputing 147, 71-82, 2015 | 336 | 2015 |
| Prototype‐based models in machine learning M Biehl, B Hammer, T Villmann Wiley Interdisciplinary Reviews: Cognitive Science 7 (2), 92-111, 2016 | 241 | 2016 |
| Neural maps in remote sensing image analysis T Villmann, E Merényi, B Hammer Neural Networks 16 (3-4), 389-403, 2003 | 236 | 2003 |
| Batch and median neural gas M Cottrell, B Hammer, A Hasenfuß, T Villmann Neural Networks 19 (6-7), 762-771, 2006 | 208 | 2006 |
| On the approximation capability of recurrent neural networks B Hammer Neurocomputing 31 (1-4), 107-123, 2000 | 205 | 2000 |
| Supervised neural gas with general similarity measure B Hammer, M Strickert, T Villmann Neural Processing Letters 21 (1), 21-44, 2005 | 202 | 2005 |
| Merge SOM for temporal data M Strickert, B Hammer Neurocomputing 64, 39-71, 2005 | 183 | 2005 |
| Recursive self-organizing network models B Hammer, A Micheli, A Sperduti, M Strickert Neural Networks 17 (8-9), 1061-1085, 2004 | 178 | 2004 |
| A note on the universal approximation capability of support vector machines B Hammer, K Gersmann neural processing letters 17 (1), 43-53, 2003 | 162 | 2003 |
| Explanation as a social practice: Toward a conceptual framework for the social design of AI systems KJ Rohlfing, P Cimiano, I Scharlau, T Matzner, HM Buhl, H Buschmeier, ... IEEE Transactions on Cognitive and Developmental Systems 13 (3), 717-728, 2020 | 161 | 2020 |
| Limited rank matrix learning, discriminative dimension reduction and visualization K Bunte, P Schneider, B Hammer, FM Schleif, T Villmann, M Biehl Neural Networks 26, 159-173, 2012 | 160 | 2012 |
| Distance learning in discriminative vector quantization P Schneider, M Biehl, B Hammer Neural computation 21 (10), 2942-2969, 2009 | 148 | 2009 |
| Dynamics and Generalization Ability of LVQ Algorithms. M Biehl, A Ghosh, B Hammer Journal of Machine Learning Research 8 (2), 2007 | 146 | 2007 |
| A general framework for dimensionality-reducing data visualization mapping K Bunte, M Biehl, B Hammer Neural Computation 24 (3), 771-804, 2012 | 144 | 2012 |
| A general framework for unsupervised processing of structured data B Hammer, A Micheli, A Sperduti, M Strickert Neurocomputing 57, 3-35, 2004 | 136 | 2004 |
Thomas VillmannProfessor for Mathematics and Computational Intelligence, University of Applied Sciences MittweidaVerified email at hs-mittweida.de
