36 results
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**Maschinelles Lernen: Entwicklung ohne Grenzen?**
In *Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen*, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)

**Methods in Psychophysics**
In *Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience*, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

**Transfer Learning for BCIs**
In *Brain–Computer Interfaces Handbook*, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

**Nonlinear functional causal models for distinguishing cause from effect**
In *Statistics and Causality: Methods for Applied Empirical Research*, pages: 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (inbook)

**A cognitive brain–computer interface for patients with amyotrophic lateral sclerosis**
In *Brain-Computer Interfaces: Lab Experiments to Real-World Applications*, 228(Supplement C):221-239, 8, Progress in Brain Research, (Editors: Damien Coyle), Elsevier, 2016 (incollection)

**High Gamma-Power Predicts Performance in Brain-Computer Interfacing**
(3), Max-Planck-Institut für Intelligente Systeme, Tübingen, February 2012 (techreport)

**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

**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

**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)

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

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

**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

**Popper, Falsification and the VC-dimension**
(145), Max Planck Institute for Biological Cybernetics, November 2005 (techreport)

**A Combinatorial View of Graph Laplacians**
(144), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2005 (techreport)

**Beyond Pairwise Classification and Clustering Using Hypergraphs**
(143), Max Planck Institute for Biological Cybernetics, August 2005 (techreport)

**Generalized Nonnegative Matrix Approximations using Bregman Divergences**
Univ. of Texas at Austin, June 2005 (techreport)

**Measuring Statistical Dependence with Hilbert-Schmidt Norms**
(140), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2005 (techreport)

**Consistency of Kernel Canonical Correlation Analysis**
(942), Institute of Statistical Mathematics, 4-6-7 Minami-azabu, Minato-ku, Tokyo 106-8569 Japan, June 2005 (techreport)

**Approximate Inference for Robust Gaussian Process Regression**
(136), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)

**Support Vector Machines and Kernel Algorithms**
In *Encyclopedia of Biostatistics (2nd edition), Vol. 8*, 8, pages: 5328-5335, (Editors: P Armitage and T Colton), John Wiley & Sons, NY USA, 2005 (inbook)

**Visual perception
I: Basic principles**
In *Handbook of Cognition*, pages: 3-47, (Editors: Lamberts, K. , R. Goldstone), Sage, London, 2005 (inbook)

**Maximum-Margin Feature Combination
for Detection and Categorization**
Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)

**Towards a Statistical Theory of Clustering. Presented at the PASCAL workshop on clustering, London**
Presented at the PASCAL workshop on clustering, London, 2005 (techreport)

**Approximate Bayesian Inference for Psychometric Functions using MCMC Sampling**
(135), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)

**Kernel Dependency Estimation**
(98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)

**Global Geometry of SVM Classifiers**
Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2002 (techreport)

**Computationally Efficient Face Detection**
(MSR-TR-2002-69), Microsoft Research, June 2002 (techreport)

**Kernel-based nonlinear blind source separation**
EU-Project BLISS, January 2002 (techreport)

**A compression approach to support vector model selection**
(101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)

**Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design**
Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)

**Observations on the Nyström Method for Gaussian Process Prediction**
Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2002 (techreport)

**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)

**Estimating the support of a high-dimensional distribution**
(MSR-TR-99-87), Microsoft Research, 1999 (techreport)

**Generalization Bounds via Eigenvalues of the Gram matrix**
(99-035), NeuroCOLT, 1999 (techreport)

**Sparse kernel feature analysis**
(99-04), Data Mining Institute, 1999, 24th Annual Conference of Gesellschaft f{\"u}r Klassifikation, University of Passau (techreport)

**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)