Grosse-Wentrup, M., Schölkopf, B. A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs) In Brain-Computer Interface Research, pages: 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 (inbook)

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Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J. Semi-supervised learning in causal and anticausal settings In Empirical Inference, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

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Sra, S. Tractable large-scale optimization in machine learning In Tractability: Practical Approaches to Hard Problems, pages: 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (inbook)

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Seldin, Y., Schölkopf, B. On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension In Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, pages: 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

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Toussaint, M., Storkey, A., Harmeling, S. Expectation-Maximization methods for solving (PO)MDPs and optimal control problems In Inference and Learning in Dynamic Models, (Editors: Barber, D., Cemgil, A.T. and Chiappa, S.), Cambridge University Press, Cambridge, UK, January 2012 (inbook) In press

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Bruzzone, L., Persello, C., Demir, B. Active Learning Methods in Classification of Remote Sensing Images In Signal and Image Processing for Remote Sensing, (Editors: CH Chen), CRC Press, Boca Raton, FL, USA, January 2012 (inbook) In press

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Habeck, M. Inferential structure determination from NMR data In Bayesian methods in structural bioinformatics, pages: 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 (inbook)

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Sigaud, O., Peters, J. Robot Learning In Encyclopedia of the sciences of learning, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 (inbook)

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Kober, J., Peters, J. Reinforcement Learning in Robotics: A Survey In Reinforcement Learning, 12, pages: 579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012 (inbook)

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Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L. Higher-Order Tensors in Diffusion MRI In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, (Editors: Westin, C. F., Vilanova, A. and Burgeth, B.), Springer, 2012 (inbook) Accepted

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Schölkopf, B., Smola, A. Support Vector Machines In Handbook of Brain Theory and Neural Networks (2nd edition), pages: 1119-1125, (Editors: MA Arbib), MIT Press, Cambridge, MA, USA, 2003 (inbook)

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Perez-Cruz, F., Weston, J., Herrmann, D., Schölkopf, B. Extension of the nu-SVM range for classification In Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, Vol. 190, 190, pages: 179-196, NATO Science Series III: Computer and Systems Sciences, (Editors: J Suykens and G Horvath and S Basu and C Micchelli and J Vandewalle), IOS Press, Amsterdam, 2003 (inbook)

[BibTex]

Schölkopf, B. An Introduction to Support Vector Machines In Recent Advances and Trends in Nonparametric Statistics , pages: 3-17, (Editors: MG Akritas and DN Politis), Elsevier, Amsterdam, The Netherlands, 2003 (inbook)

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Schölkopf, B., Guyon, I., Weston, J. Statistical Learning and Kernel Methods in Bioinformatics In Artificial Intelligence and Heuristic Methods in Bioinformatics, 183, pages: 1-21, 3, (Editors: P Frasconi und R Shamir), IOS Press, Amsterdam, The Netherlands, 2003 (inbook)

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Navia-Vázquez, A., Schölkopf, B. Statistical Learning and Kernel Methods In Adaptivity and Learning—An Interdisciplinary Debate, pages: 161-186, (Editors: R.Kühn and R Menzel and W Menzel and U Ratsch and MM Richter and I-O Stamatescu), Springer, Berlin, Heidelberg, Germany, 2003 (inbook)

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Schölkopf, B., Smola, A. A Short Introduction to Learning with Kernels In Proceedings of the Machine Learning Summer School, Lecture Notes in Artificial Intelligence, Vol. 2600, pages: 41-64, LNAI 2600, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Heidelberg, Germany, 2003 (inbook)

[BibTex]

Smola, A., Schölkopf, B. Bayesian Kernel Methods In Advanced Lectures on Machine Learning, Machine Learning Summer School 2002, Lecture Notes in Computer Science, Vol. 2600, LNAI 2600, pages: 65-117, 0, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Germany, 2003 (inbook)

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Elisseeff, A., Pontil, M. Stability of ensembles of kernel machines In 190, pages: 111-124, NATO Science Series III: Computer and Systems Science, (Editors: Suykens, J., G. Horvath, S. Basu, C. Micchelli and J. Vandewalle), IOS press, Netherlands, 2003 (inbook)

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Schölkopf, B., Smola, A., Müller, K. Kernel principal component analysis. In Advances in Kernel Methods—Support Vector Learning, pages: 327-352, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

[BibTex]

Williamson, R., Smola, A., Schölkopf, B. Entropy numbers, operators and support vector kernels. In Advances in Kernel Methods - Support Vector Learning, pages: 127-144, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

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