Generalized relevance learning vector quantization B Hammer, T Villmann Neural Networks 15 (8-9), 1059-1068, 2002 | 570 | 2002 |
Adaptive relevance matrices in learning vector quantization P Schneider, M Biehl, B Hammer Neural computation 21 (12), 3532-3561, 2009 | 455 | 2009 |
Incremental learning algorithms and applications A Geppert, B Hammer ESANN, 2016 | 442 | 2016 |
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 | 381 | 2018 |
Parametric nonlinear dimensionality reduction using kernel t-SNE A Gisbrecht, A Schulz, B Hammer Neurocomputing 147, 71-82, 2015 | 274 | 2015 |
KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift V Losing, B Hammer, H Wersing IEEE International Conference on Data Mining (ICDM), 2016 | 271 | 2016 |
Neural maps in remote sensing image analysis T Villmann, E Merényi, B Hammer Neural Networks 16 (3-4), 389-403, 2003 | 234 | 2003 |
Batch and median neural gas M Cottrell, B Hammer, A Hasenfuß, T Villmann Neural Networks 19 (6-7), 762-771, 2006 | 196 | 2006 |
Supervised neural gas with general similarity measure B Hammer, M Strickert, T Villmann Neural Processing Letters 21, 21-44, 2005 | 195 | 2005 |
On the approximation capability of recurrent neural networks B Hammer Neurocomputing 31 (1-4), 107-123, 2000 | 179 | 2000 |
Merge SOM for temporal data M Strickert, B Hammer Neurocomputing 64, 39-71, 2005 | 178 | 2005 |
Prototype‐based models in machine learning M Biehl, B Hammer, T Villmann Wiley Interdisciplinary Reviews: Cognitive Science 7 (2), 92-111, 2016 | 176 | 2016 |
Recursive self-organizing network models B Hammer, A Micheli, A Sperduti, M Strickert Neural Networks 17 (8-9), 1061-1085, 2004 | 171 | 2004 |
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 | 150 | 2012 |
Distance learning in discriminative vector quantization P Schneider, M Biehl, B Hammer Neural computation 21 (10), 2942-2969, 2009 | 146 | 2009 |
A note on the universal approximation capability of support vector machines B Hammer, K Gersmann neural processing letters 17, 43-53, 2003 | 146 | 2003 |
Dynamics and Generalization Ability of LVQ Algorithms. M Biehl, A Ghosh, B Hammer Journal of Machine Learning Research 8 (2), 2007 | 142 | 2007 |
A general framework for unsupervised processing of structured data B Hammer, A Micheli, A Sperduti, M Strickert Neurocomputing 57, 3-35, 2004 | 133 | 2004 |
A general framework for dimensionality-reducing data visualization mapping K Bunte, M Biehl, B Hammer Neural Computation 24 (3), 771-804, 2012 | 132 | 2012 |
Perspectives of neural-symbolic integration B Hammer, P Hitzler Springer, 2007 | 123 | 2007 |