Klaus-Robert Müller

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2011
33Insights from Classifying Visual Concepts with Multiple Kernel Learning. Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Müller, Wojciech Samek, Ulf Brefeld, Klaus-Robert Müller, Motoaki Kawanabe. CoRR (abs/1112.3697) (2011). Web SearchBibTeXDownload
2010
32Approximate Tree Kernels. Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Robert Müller. Journal of Machine Learning Research (11): 555-580 (2010). Web SearchBibTeXDownload
31Comparison of Granger Causality and Phase Slope Index. Guido Nolte, Andreas Ziehe, Nicole Krämer, Florin Popescu, Klaus-Robert Müller. Journal of Machine Learning Research - Proceedings Track (6): 267-276 (2010). Web SearchBibTeXDownload
30Sparse Causal Discovery in Multivariate Time Series. Stefan Haufe, Klaus-Robert Müller, Guido Nolte, Nicole Krämer. Journal of Machine Learning Research - Proceedings Track (6): 97-106 (2010). Web SearchBibTeXDownload
2009
29Efficient and Accurate Lp-Norm Multiple Kernel Learning. Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Pavel Laskov, Klaus-Robert Müller, Alexander Zien. NIPS 2009, 997-1005. Web SearchBibTeXDownload
2008
28On Relevant Dimensions in Kernel Feature Spaces. Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert Müller. Journal of Machine Learning Research (9): 1875-1908 (2008). Web SearchBibTeXDownload
2007
27The Need for Open Source Software in Machine Learning. Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson. Journal of Machine Learning Research (8): 2443-2466 (2007). Web SearchBibTeXDownload
26Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning. Gunnar Rätsch, Sören Sonnenburg, Jagan Srinivasan, Hanh Witte, Klaus-Robert Müller, Ralf J. Sommer, Bernhard Schölkopf. PLoS Computational Biology (3) (2007). Web SearchBibTeXDownload
2006
25Denoising and Dimension Reduction in Feature Space. Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert Müller. NIPS 2006, 185-192. Web SearchBibTeXDownload
24On the information and representation of non-Euclidean pairwise data. Julian Laub, Volker Roth, Joachim M. Buhmann, Klaus-Robert Müller. Pattern Recognition (39): 1815-1826 (2006). Web SearchBibTeXDownload
2004
23Asymptotic Properties of the Fisher Kernel. Koji Tsuda, Shotaro Akaho, Motoaki Kawanabe, Klaus-Robert Müller. Neural Computation (16): 115-137 (2004). Web SearchBibTeXDownload
2003
22Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller. IEEE Trans. Pattern Anal. Mach. Intell. (25): 623-633 (2003). Web SearchBibTeXDownload
2002
21Subspace information criterion for nonquadratic regularizers-Model selection for sparse regressors. Koji Tsuda, Masashi Sugiyama, Klaus-Robert Müller. IEEE Transactions on Neural Networks (13): 70-80 (2002). Web SearchBibTeXDownload
20Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification. Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Klaus-Robert Müller. IEEE Trans. Pattern Anal. Mach. Intell. (24): 1184-1199 (2002). Web SearchBibTeXDownload
19A New Discriminative Kernel from Probabilistic Models. Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller. Neural Computation (14): 2397-2414 (2002). Web SearchBibTeXDownload
18Going Metric: Denoising Pairwise Data. Volker Roth, Julian Laub, Joachim M. Buhmann, Klaus-Robert Müller. NIPS 2002, 817-824. Web SearchBibTeXDownload
17Clustering with the Fisher Score. Koji Tsuda, Motoaki Kawanabe, Klaus-Robert Müller. NIPS 2002, 729-736. Web SearchBibTeXDownload
2001
16Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers. Koji Tsuda, Gunnar Rätsch, Sebastian Mika, Klaus-Robert Müller. ICANN 2001, 331-338. Web SearchBibTeXDownload
15An introduction to kernel-based learning algorithms. Klaus-Robert Müller, Sebastian Mika, Gunnar Rätsch, Koji Tsuda, Bernhard Schölkopf. IEEE Transactions on Neural Networks (12): 181-201 (2001). Web SearchBibTeXDownload
14A New Discriminative Kernel From Probabilistic Models. Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller. NIPS 2001, 977-984. Web SearchBibTeXDownload
2000
13Engineering support vector machine kernels that recognize translation initiation sites. Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Thomas Lengauer, Klaus-Robert Müller. Bioinformatics (16): 799-807 (2000). Web SearchBibTeXDownload
12Robust Ensemble Learning for Data Mining. Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller. PAKDD 2000, 341-344. Web SearchBibTeXDownload
1999
11Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites. Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Christian Lemmen, Alex J. Smola, Thomas Lengauer, Klaus-Robert Müller. German Conference on Bioinformatics 1999, 37-43. Web SearchBibTeX
10Input space versus feature space in kernel-based methods. Bernhard Schölkopf, Sebastian Mika, Christopher J. C. Burges, Phil Knirsch, Klaus-Robert Müller, Gunnar Rätsch, Alexander J. Smola. IEEE Transactions on Neural Networks (10): 1000-1017 (1999). Web SearchBibTeXDownload
9Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. Bernhard Schölkopf, Klaus-Robert Müller, Alex J. Smola. Inform., Forsch. Entwickl. (14): 154-163 (1999). Web SearchBibTeXDownload
8v-Arc: Ensemble Learning in the Presence of Outliers. Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika. NIPS 1999, 561-567. Web SearchBibTeXDownload
7Invariant Feature Extraction and Classification in Kernel Spaces. Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller. NIPS 1999, 526-532. Web SearchBibTeXDownload
1998
6Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller. Neural Computation (10): 1299-1319 (1998). Web SearchBibTeXDownload
5The connection between regularization operators and support vector kernels. Alex J. Smola, Bernhard Schölkopf, Klaus-Robert Müller. Neural Networks (11): 637-649 (1998). Web SearchBibTeXDownload
4Kernel PCA and De-Noising in Feature Spaces. Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch. NIPS 1998, 536-542. Web SearchBibTeXDownload
1997
3Predicting Time Series with Support Vector Machines. Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik. ICANN 1997, 999-1004. Web SearchBibTeXDownload
2Kernel Principal Component Analysis. Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller. ICANN 1997, 583-588. Web SearchBibTeXDownload
1996
1Effiicient BackProp. Yann LeCun, Léon Bottou, Genevieve B. Orr, Klaus-Robert Müller. Neural Networks: Tricks of the Trade 1996, 9-50. Web SearchBibTeXDownload
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