| Stability-based validation of clustering solutions T Lange, V Roth, ML Braun, JM Buhmann Neural computation 16 (6), 1299-1323, 2004 | 544 | 2004 |
| Implementing reproducible research V Stodden, F Leisch, RD Peng CRC Press, 2014 | 274 | 2014 |
| The need for open source software in machine learning S Sonnenburg, ML Braun, CS Ong, S Bengio, L Bottou, G Holmes, ... JMLR 8, 2443-2466, 2007 | 224 | 2007 |
| Improving human performance in a real operating environment through real-time mental workload detection J Kohlmorgen, G Dornhege, M Braun, B Blankertz, KR Müller, G Curio, ... Toward brain-computer interfacing 409422, 409-422, 2007 | 208 | 2007 |
| A resampling approach to cluster validation V Roth, T Lange, M Braun, J Buhmann Compstat, 123-128, 2002 | 181 | 2002 |
| Stability-based model selection T Lange, M Braun, V Roth, J Buhmann Advances in neural information processing systems 15, 633-642, 2002 | 144 | 2002 |
| On relevant dimensions in kernel feature spaces ML Braun, JM Buhmann, KR Müller The Journal of Machine Learning Research 9, 1875-1908, 2008 | 132 | 2008 |
| Kernel Analysis of Deep Networks. G Montavon, ML Braun, KR Müller Journal of Machine Learning Research 12 (9), 2011 | 127 | 2011 |
| Accurate error bounds for the eigenvalues of the kernel matrix ML Braun The Journal of Machine Learning Research 7, 2303-2328, 2006 | 60 | 2006 |
| Hidden markov anomaly detection N Görnitz, M Braun, M Kloft International conference on machine learning, 1833-1842, 2015 | 51 | 2015 |
| Analyzing local structure in kernel-based learning: Explanation, complexity, and reliability assessment G Montavon, ML Braun, T Krueger, KR Muller IEEE Signal Processing Magazine 30 (4), 62-74, 2013 | 46 | 2013 |
| Kernelizing PLS, degrees of freedom, and efficient model selection N Krämer, ML Braun Proceedings of the 24th international conference on Machine learning, 441-448, 2007 | 41 | 2007 |
| Fast cross-validation via sequential testing. T Krueger, D Panknin, ML Braun J. Mach. Learn. Res. 16 (1), 1103-1155, 2015 | 40 | 2015 |
| Spectral properties of the kernel matrix and their relation to kernel methods in machine learning ML Braun Universitäts-und Landesbibliothek Bonn, 2005 | 32 | 2005 |
| Estimating noise and dimensionality in BCI data sets: Towards illiteracy comprehension C Sannelli, M Braun, M Tangermann, KR Müller na 1, 1-6, 2008 | 30 | 2008 |
| Improving BCI performance by task-related trial pruning C Sannelli, M Braun, KR Müller Neural Networks 22 (9), 1295-1304, 2009 | 25 | 2009 |
| Stability-based model order selection in clustering with applications to gene expression data V Roth, ML Braun, T Lange, JM Buhmann International Conference on Artificial Neural Networks, 607-612, 2002 | 23 | 2002 |
| Learning and evaluation in presence of non-iid label noise N Görnitz, A Porbadnigk, A Binder, C Sannelli, M Braun, KR Müller, ... Artificial Intelligence and Statistics, 293-302, 2014 | 22 | 2014 |
| Extracting latent brain states—Towards true labels in cognitive neuroscience experiments AK Porbadnigk, N Görnitz, C Sannelli, A Binder, M Braun, M Kloft, ... NeuroImage 120, 225-253, 2015 | 19 | 2015 |
| Deep Boltzmann machines as feed-forward hierarchies G Montavon, M Braun, KR Muller Artificial Intelligence and Statistics, 798-804, 2012 | 19 | 2012 |
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
