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)

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

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)

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)

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

Lee, S.-W.; Bülthoff, H.; Müller, K.-R. (Hg.): Recent Progress in Brain and Cognitive Engineering. Springer, Dordrecht, The Netherlands (2015), 213 S.

Müller, K.; Mika, S.; Rätsch, G.; Tsuda, K.; Schölkopf, B.: An Introduction to Kernel-Based Learning Algorithms. In: Handbook of neural network signal processing: Neural network signal processing, 4, S. 95 - 134 (Hg. Hu, Y.; Hwang, J.-N.; Perry, S.). CRC Press, Boca Raton, FL, USA (2002)

Biessmann, F.; Murayama, Y.; Logothetis, N.; Müller, K.-R.; Meinecke, F.: Non-separable Spatiotemporal Brain Hemodynamics Contain Neural Information. In: Machine Learning and Interpretation in Neuroimaging, S. 140 - 147 (Hg. Langs, G.; Rish, I.; Murphy, B.). NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011), Sierra Nevada, Spain, 16. Dezember 2011 - 17. Dezember 2011. Springer, Berlin, Germany (2012)

Laub, J.; Macke, J.; Müller, K.-R.; Wichmann, F.: Inducing Metric Violations in Human Similarity Judgements. In: Advances in Neural Information Processing Systems 19, S. 777 - 784 (Hg. Schölkopf, B.; Platt, J.; Hoffman, T.). Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), Vancouver, BC, Canada. MIT Press, Cambridge, MA, USA (2007)

Müller, K.-R.; Smola, A.; Rätsch, G.; Schölkopf, B.; Kohlmorgen, J.; Vapnik, V.: Using support vector machines for time series prediction. In: Advances in kernel methods: support vector learning, S. 243 - 253 (Hg. Schölkopf, B.; Burges, C.; Smola, A.). Eleventh Annual Conference on Neural Information Processing (NIPS 1997), Breckenridge, CO, USA, 01. Dezember 1997 - 06. Dezember 1997. MIT Press, Cambridge, MA, USA (1999)

Schölkopf, B.; Smola, A.; Müller, K.-R.: Kernel principal component analysis. In: Advances in kernel methods: support vector learning, S. 327 - 352 (Hg. Schölkopf, B.; Burges, C.; Smola, A.). Eleventh Annual Conference on Neural Information Processing (NIPS 1997), Breckenridge, CO, USA, 01. Dezember 1997 - 06. Dezember 1997. MIT Press, Cambridge, MA, USA (1999)

Müller, K.-R.; Smola, A.; Rätsch, G.; Schölkopf, B.; Kohlmorgen, J.; Vapnik, V.: Predicting time series with support vector machines. In: Artificial Neural Networks - ICANN'97: 7th International Conference Lausanne, Switzerland, October 8–10, 1997, S. 999 - 1004 (Hg. Gerstner, W.; Germond, A.; Hasler, M.; Nicoud, J.-D.). 7th International Conference on Artificial Neural Networks (ICANN 1997), Lausanne, Switzerland, 08. Oktober 1997 - 10. Oktober 1997. Springer, Berlin, Germany (1997)

Schölkopf, B.; Smola, A.; Müller, K.-R.: Kernel principal component analysis. In: Artificial Neural Networks - ICANN'97: 7th International Conference Lausanne, Switzerland, October 8–10, 1997, S. 583 - 588 (Hg. Gerstner, W.; Germond, A.; Hasler, M.; Nicoud, J.-D.). 7th International Conference on Artificial Neural Networks (ICANN 1997), Lausanne, Switzerland, 08. Oktober 1997 - 10. Oktober 1997. Springer, Berlin, Germany (1997)