Publikationen von K-R Müller

Kim, J.; Müller, K.-R.; Chung, Y.; Chung, S.-C.; Park, J.-Y.; Bülthoff, H.; Kim, S.-P.: Distributed functions of detection and discrimination of vibrotactile stimuli in the hierarchical human somatosensory system. Frontiers in Human Neuroscience 8, 1070, S. 1 - 10 (2015)

Biessmann, F.; Murayama, Y.; Logothetis, N.; Müller, K.; Meinecke, F.: Improved decoding of neural activity from fMRI signals using non-separable spatiotemporal deconvolutions. NeuroImage 61 (4), S. 1031 - 1042 (2012)

Murayama, Y.; Biessmann, F.; Meinecke, F.; Müller, K.-R.; Augath, M.; Oeltermann, A.; Logothetis, N.: Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA. Magnetic Resonance Imaging 28 (8), S. 1095 - 1103 (2010)

Baehrens, D.; Schroeter, T.; Harmeling, S.; Kawanabe, M.; Hansen, K.; Müller, K.-R.: How to Explain Individual Classification Decisions. Journal of Machine Learning Research 11, S. 1803 - 1831 (2010)

Biessmann, F.; Meinecke, F.; Gretton, A.; Rauch, A.; Rainer, G.; Logothetis, N.; Müller, K.-R.: Temporal Kernel CCA and its Application in Multimodal Neuronal Data Analysis. Machine Learning 79 (1-2), S. 5 - 27 (2010)

Sonnenburg, S.; Braun, M.; Ong, C.; Bengio, S.; Bottou, L.; Holmes , G.; LeCun, Y.; Müller, K.-R.; Pereira, F.; Rasmussen, C. et al.; Rätsch, G.; Schölkopf, B.; Smola, A.; Vincent, P.; Weston, J.; Williamson, R.: The Need for Open Source Software in Machine Learning. The Journal of Machine Learning Research 8, S. 2443 - 2466 (2007)

Rätsch, G.; Sonnenburg, S.; Srinivasan, J.; Witte, H.; Müller, K.-R.; Sommer, R.-J.; Schölkopf, B.: Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning. PLoS Computational Biology 3 (2 ), S. 0313 - 0322 (2007)

Harmeling, S.; Dornhege, G.; Tax, D.; Meinecke, F.; Müller, K.-R.: From outliers to prototypes: Ordering data. Neurocomputing 69 (13-15), S. 1608 - 1618 (2006)

Meinecke, F.; Harmeling, S.; Müller, K.-R.: Inlier-based ICA with an application to superimposed images. International Journal of Imaging Systems and Technology 15 (1), S. 48 - 55 (2005)

Harmeling, S.; Meinecke, F.; Müller, K.-R.: Injecting noise for analysing the stability of ICA components. Signal Processing 84 (2), S. 255 - 266 (2004)

Tsuda, K.; Akaho, S.; Kawanabe, M.; Müller, K.: Asymptotic Properties of the Fisher Kernel. Neural computation 16 (1), S. 115 - 137 (2004)

Ziehe, A.; Kawanabe, M.; Harmeling, S.; Müller, K.-R.: Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation. The Journal of Machine Learning Research 4, S. 1319 - 1338 (2003)

Harmeling, S.; Ziehe, A.; Kawanabe, M.; Müller, K.-R.: Kernel-based nonlinear blind source separation. Neural computation 15 (5), S. 1089 - 1124 (2003)

Mika, S.; Rätsch, G.; Weston, J.; Schölkopf, B.; Smola, A.; Müller, K.-R.: Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (5), S. 623 - 628 (2003)

Tsuda, K.; Kawanabe, M.; Rätsch, G.; Sonnenburg, S.; Müller, K.: A New Discriminative Kernel from Probabilistic Models. Neural computation 14 (10), S. 2397 - 2414 (2002)

Rätsch, G.; Mika, S.; Schölkopf, B.; Müller, K.-R.: Constructing Boosting algorithms from SVMs: an application to one-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (9), S. 1184 - 1199 (2002)

Müller, K.-R.; Mika, S.; Rätsch, G.; Tsuda, K.; Schölkopf, B.: An Introduction to Kernel-Based Learning Algorithms. IEEE Transactions on Neural Networks 12 (2), S. 181 - 201 (2001)

Schölkopf, B.; Mika, S.; Burges, C.; Knirsch, P.; Müller, K.-R.; Rätsch, G.; Smola, A.: Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks 10 (5), S. 1000 - 1017 (1999)

Schölkopf, B.; Müller, K.-R.; Smola, A.: Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. Informatik - Forschung und Entwicklung 14 (3), S. 154 - 163 (1999)

Schölkopf, B.; Smola, A.; Müller, K.-R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural computation 10 (5), S. 1299 - 1319 (1998)