34 results
(View BibTeX file of all listed publications)

**Machine Learning for Brain-Computer Interfaces**
Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

**PAC-Bayesian Approach to Formulation of Clustering Objectives**
NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

**Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data**
Women in Machine Learning Workshop (WiML), December 2009 (talk)

**Event-Related Potentials in Brain-Computer Interfacing**
Invited lecture on the bachelor & masters course "Introduction to Brain-Computer Interfacing", October 2009 (talk)

**BCI2000 and Python**
Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

**Implementing a Signal Processing Filter in BCI2000 Using C++**
Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

**Toward a Theory of Consciousness**
In *The Cognitive Neurosciences*, pages: 1201-1220, (Editors: Gazzaniga, M.S.), MIT Press, Cambridge, MA, USA, October 2009 (inbook)

**Randomized algorithms for statistical image analysis based on percolation theory**
27th European Meeting of Statisticians (EMS), July 2009 (talk)

**Learning Motor Primitives for Robotics**
Advanced Telecommunications Research Center ATR, June 2009 (talk)

**Text Clustering with Mixture of von Mises-Fisher Distributions**
In *Text mining: classification, clustering, and applications*, pages: 121-161, Chapman & Hall/CRC data mining and knowledge discovery series, (Editors: Srivastava, A. N. and Sahami, M.), CRC Press, Boca Raton, FL, USA, June 2009 (inbook)

**Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer**
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009 (talk)

**Data Mining for Biologists**
In *Biological Data Mining in Protein Interaction Networks*, pages: 14-27, (Editors: Li, X. and Ng, S.-K.), Medical Information Science Reference, Hershey, PA, USA, May 2009 (inbook)

**Large Margin Methods for Part of Speech Tagging**
In *Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods*, pages: 141-160, (Editors: Keshet, J. and Bengio, S.), Wiley, Hoboken, NJ, USA, January 2009 (inbook)

**Covariate shift and local learning by distribution matching**
In *Dataset Shift in Machine Learning*, pages: 131-160, (Editors: Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, N. D.), MIT Press, Cambridge, MA, USA, 2009 (inbook)

**An introduction to Kernel Learning Algorithms**
In *Kernel Methods for Remote Sensing Data Analysis*, pages: 25-48, 2, (Editors: Gustavo Camps-Valls and Lorenzo Bruzzone), Wiley, New York, NY, USA, 2009 (inbook)

**Learning Control and Planning from the View of Control Theory and Imitation**
NIPS Workshop "Planning for the Real World: The promises and challenges of dealing with uncertainty", December 2003 (talk)

**Recurrent neural networks from learning attractor dynamics**
NIPS Workshop on RNNaissance: Recurrent Neural Networks, December 2003 (talk)

Bousquet, O.
**Statistical Learning Theory**
Machine Learning Summer School, August 2003 (talk)

**Remarks on Statistical Learning Theory**
Machine Learning Summer School, August 2003 (talk)

**Rademacher and Gaussian averages in Learning Theory**
Universite de Marne-la-Vallee, March 2003 (talk)

Bousquet, O., Schölkopf, B.
**Statistical Learning Theory**
March 2003 (talk)

**Concentration Inequalities and Data-Dependent Error Bounds**
Uni. Jena, February 2003 (talk)

**Introduction: Robots with Cognition?**
6, pages: 38, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (talk)

**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)

**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)

**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)

**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)

**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)

**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)

**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)

**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)

**Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond**
pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)

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