49 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)

**Robot Learning**
In *Springer Handbook of Robotics*, pages: 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (inbook)

**Policy Gradient Methods**
In *Encyclopedia of Machine Learning and Data Mining*, pages: 982-985, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)

**Unsupervised clustering of EOG as a viable substitute for optical eye-tracking**
In *First Workshop on Eye Tracking and Visualization (ETVIS 2015)*, pages: 151-167, Mathematics and Visualization, (Editors: Burch, M., Chuang, L., Fisher, B., Schmidt, A., and Weiskopf, D.), Springer, 2017 (inbook)

**Statistical Asymmetries Between Cause and Effect**
In *Time in Physics*, pages: 129-139, Tutorials, Schools, and Workshops in the Mathematical Sciences, (Editors: Renner, Renato and Stupar, Sandra), Springer International Publishing, Cham, 2017 (inbook)

**Robot Learning**
In *Encyclopedia of Machine Learning and Data Mining*, pages: 1106-1109, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)

**Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism**
NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

**Positional Oligomer Importance Matrices**
NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

**Machine Learning Algorithms for Polymorphism Detection**
NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

**An Automated Combination of Kernels for Predicting Protein Subcellular Localization**
NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

**Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration**
Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

**Policy Learning for Robotics**
14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

**Hilbert Space Representations of Probability Distributions**
2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

**Regression with Intervals**
International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

**Support Vector Machine Learning for Interdependent and Structured Output Spaces**
In *Predicting Structured Data*, pages: 85-104, Advances in neural information processing systems, (Editors: Bakir, G. H. , T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, S. V. N. Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

**Brisk Kernel ICA**
In *Large Scale Kernel Machines*, pages: 225-250, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

**MR-Based PET Attenuation Correction: Method and Validation**
Joint Molecular Imaging Conference, September 2007 (talk)

**Training a Support Vector Machine in the Primal**
In *Large Scale Kernel Machines*, pages: 29-50, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007, This is a slightly updated version of the Neural Computation paper (inbook)

**Approximation Methods for Gaussian Process Regression**
In *Large-Scale Kernel Machines*, pages: 203-223, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

**Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces**
In *Predicting Structured Data*, pages: 283-300, Advances in neural information processing systems, (Editors: BakIr, G. H., T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, S. V.N. Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

**Trading Convexity for Scalability**
In *Large Scale Kernel Machines*, pages: 275-300, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

**Bayesian methods for NMR structure determination**
29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

**Classifying Event-Related Desynchronization in EEG, ECoG and MEG signals**
In *Toward Brain-Computer Interfacing*, pages: 235-260, Neural Information Processing, (Editors: G Dornhege and J del R Millán and T Hinterberger and DJ McFarland and K-R Müller), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

**Joint Kernel Maps**
In *Predicting Structured Data*, pages: 67-84, Advances in neural information processing systems, (Editors: GH Bakir and T Hofmann and B Schölkopf and AJ Smola and B Taskar and SVN Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

**Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach**
In *Toward Brain-Computer Interfacing*, pages: 43-64, Neural Information Processing, (Editors: G. Dornhege and J del R Millán and T Hinterberger and DJ McFarland and K-R Müller), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

**Collaborative Filtering via Ensembles of Matrix Factorizations**
KDD Cup and Workshop, August 2007 (talk)

**Thinking Out Loud: Research and Development of Brain Computer Interfaces**
Invited keynote talk at the Max Planck Society‘s PhDNet Workshop., July 2007 (talk)

**Local Learning Algorithms for Transductive Classification, Clustering and Data Projection**
Yahoo Inc., July 2007 (talk)

**Dirichlet Process Mixtures of Factor Analysers**
Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), June 2007 (talk)

**New BCI approaches: Selective Attention to Auditory and Tactile Stimulus Streams**
Invited talk at the PASCAL Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces, June 2007 (talk)

**Towards Motor Skill Learning in Robotics**
Interactive Robot Learning - RSS workshop, June 2007 (talk)

**Transductive Support Vector Machines for Structured Variables**
International Conference on Machine Learning (ICML), June 2007 (talk)

**Impact of target-to-target interval on classification performance in the P300 speller**
Scientific Meeting "Applied Neuroscience for Healthy Brain Function", May 2007 (talk)

Peters, J.
**Benchmarking of Policy Gradient Methods**
ADPRL Workshop, April 2007 (talk)

**Probabilistic Structure Calculation**
In *Structure and Biophysics: New Technologies for Current Challenges in Biology and Beyond*, pages: 81-98, NATO Security through Science Series, (Editors: Puglisi, J. D.), Springer, Berlin, Germany, March 2007 (inbook)

**New Margin- and Evidence-Based Approaches for EEG Signal Classification**
Invited talk at the FaSor Jahressymposium, February 2007 (talk)

**On the Pre-Image Problem in Kernel Methods**
In *Kernel Methods in Bioengineering, Signal and Image Processing*, pages: 284-302, (Editors: G Camps-Valls and JL Rojo-Álvarez and M Martínez-Ramón), Idea Group Publishing, Hershey, PA, USA, January 2007 (inbook)

**Some comments on ν-SVM**
In *A tribute to Antonio Lepschy*, pages: -, (Editors: Picci, G. , M. E. Valcher), Edizione Libreria Progetto, Padova, Italy, 2007 (inbook)

Bousquet, O.
**Transductive Learning: Motivation, Models, Algorithms**
January 2002 (talk)

**Robust ensemble learning**
In *Advances in Large Margin Classifiers*, pages: 207-220, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D. Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

**Entropy numbers for convex combinations and MLPs**
In *Advances in Large Margin Classifiers*, pages: 369-387, Neural Information Processing Series, (Editors: AJ Smola and PL Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA,, October 2000 (inbook)

**Natural Regularization from Generative Models**
In *Advances in Large Margin Classifiers*, pages: 51-60, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

**Solving Satisfiability Problems with Genetic Algorithms**
In *Genetic Algorithms and Genetic Programming at Stanford 2000*, pages: 206-213, (Editors: Koza, J. R.), Stanford Bookstore, Stanford, CA, USA, June 2000 (inbook)

**Statistical Learning and Kernel Methods**
In *CISM Courses and Lectures, International Centre for Mechanical Sciences Vol.431*, *CISM Courses and Lectures, International Centre for
Mechanical Sciences*, 431(23):3-24, (Editors: G Della Riccia and H-J Lenz and R Kruse), Springer, Vienna, Data Fusion and Perception, 2000 (inbook)

**An Introduction to Kernel-Based Learning Algorithms**
In *Handbook of Neural Network Signal Processing*, 4, (Editors: Yu Hen Hu and Jang-Neng Hwang), CRC Press, 2000 (inbook)

**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)

**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)