| Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition J Stallkamp, M Schlipsing, J Salmen, C Igel Neural networks 32, 323-332, 2012 | 780 | 2012 |
| Covariance matrix adaptation for multi-objective optimization C Igel, N Hansen, S Roth Evolutionary computation 15 (1), 1-28, 2007 | 727 | 2007 |
| The German traffic sign recognition benchmark: a multi-class classification competition J Stallkamp, M Schlipsing, J Salmen, C Igel The 2011 international joint conference on neural networks, 1453-1460, 2011 | 599 | 2011 |
| Evolutionary tuning of multiple SVM parameters F Friedrichs, C Igel Neurocomputing 64, 107-117, 2005 | 534 | 2005 |
| Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network A Prasoon, K Petersen, C Igel, F Lauze, E Dam, M Nielsen International conference on medical image computing and computer-assisted …, 2013 | 493 | 2013 |
| An introduction to restricted Boltzmann machines A Fischer, C Igel Iberoamerican congress on pattern recognition, 14-36, 2012 | 477 | 2012 |
| Empirical evaluation of the improved Rprop learning algorithms C Igel, M Hüsken Neurocomputing 50, 105-123, 2003 | 477 | 2003 |
| Improving the Rprop learning algorithm C Igel, M Hüsken Proceedings of the second international ICSC symposium on neural computation …, 2000 | 459 | 2000 |
| Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark S Houben, J Stallkamp, J Salmen, M Schlipsing, C Igel The 2013 international joint conference on neural networks (IJCNN), 1-8, 2013 | 433 | 2013 |
| Training restricted Boltzmann machines: An introduction A Fischer, C Igel Pattern Recognition 47 (1), 25-39, 2014 | 394 | 2014 |
| Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring M Kallenberg, K Petersen, M Nielsen, AY Ng, P Diao, C Igel, CM Vachon, ... IEEE transactions on medical imaging 35 (5), 1322-1331, 2016 | 268 | 2016 |
| Shark C Igel, V Heidrich-Meisner, T Glasmachers Journal of machine learning research 9 (Jun), 993-996, 2008 | 237 | 2008 |
| A computational efficient covariance matrix update and a (1+ 1)-CMA for evolution strategies C Igel, T Suttorp, N Hansen Proceedings of the 8th annual conference on Genetic and evolutionary …, 2006 | 168 | 2006 |
| Neuroevolution for reinforcement learning using evolution strategies C Igel The 2003 Congress on Evolutionary Computation, 2003. CEC'03. 4, 2588-2595, 2003 | 167 | 2003 |
| Image processing and behavior planning for intelligent vehicles T Bucher, C Curio, J Edelbrunner, C Igel, D Kastrup, I Leefken, G Lorenz, ... IEEE Transactions on Industrial electronics 50 (1), 62-75, 2003 | 162 | 2003 |
| A no-free-lunch theorem for non-uniform distributions of target functions C Igel, M Toussaint Journal of Mathematical Modelling and Algorithms 3 (4), 313-322, 2005 | 152 | 2005 |
| Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search V Heidrich-Meisner, C Igel Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 150 | 2009 |
| On classes of functions for which no free lunch results hold C Igel, M Toussaint Information Processing Letters 86 (6), 317-321, 2003 | 125 | 2003 |
| Efficient covariance matrix update for variable metric evolution strategies T Suttorp, N Hansen, C Igel Machine Learning 75 (2), 167-197, 2009 | 116 | 2009 |
| Maximum-gain working set selection for SVMs T Glasmachers, C Igel Journal of Machine Learning Research 7 (Jul), 1437-1466, 2006 | 100 | 2006 |
