68 results
(BibTeX)

**Prototype Classification: Insights from Machine Learning**
*Neural Computation*, 21(1):272-300, January 2009 (article)

**Consistency of Spectral Clustering**
*Annals of Statistics*, 36(2):555-586, April 2008 (article)

**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)

**Evaluating Predictive Uncertainty Challenge**
In *Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment*, pages: 1-27, (Editors: J Quiñonero Candela and I Dagan and B Magnini and F d’Alché-Buc), Springer, Berlin, Germany, First PASCAL Machine Learning Challenges Workshop (MLCW), April 2006 (inproceedings)

**Statistical Properties of Kernel Principal Component Analysis**
*Machine Learning*, 66(2-3):259-294, March 2006 (article)

**Comment on “Support vector machines with applications” by J. M. Moguerza and A. Muñoz
**
*Statistical Science*, 21(3):337-340, August 2006 (article)

**Local Rademacher Complexities**
*The Annals of Statistics*, 33(4):1497-1537, August 2005 (article)

**Kernel Methods for Measuring Independence**
*Journal of Machine Learning Research*, 6, pages: 2075-2129, December 2005 (article)

**Joint Kernel Maps**
In *Proceedings of the 8th InternationalWork-Conference on Artificial Neural Networks*, LNCS 3512, pages: 176-191, (Editors: J Cabestany and A Prieto and F Sandoval), Springer, Berlin Heidelberg, Germany, IWANN, 2005 (inproceedings)

**Theory of Classification: A Survey of Some Recent Advances**
*ESAIM: Probability and Statistics*, 9, pages: 323 , 2005 (article)

**Maximal Margin Classification for Metric Spaces**
*Journal of Computer and System Sciences*, 71(3):333-359, October 2005 (article)

**Moment Inequalities for Functions of Independent Random Variables**
*To appear in Annals of Probability*, 33, pages: 514-560, 2005 (article)

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

**Measuring Statistical Dependence with Hilbert-Schmidt Norms**
In *Algorithmic Learning Theory, Lecture Notes in Computer Science, Vol. 3734*, pages: 63-78, (Editors: S Jain and H-U Simon and E Tomita), Springer, Berlin, Germany, 16th International Conference ALT, October 2005 (inproceedings)

**Limits of Spectral Clustering**
In *Advances in Neural Information Processing Systems 17*, pages: 857-864, (Editors: Saul, L. K., Y. Weiss, L. Bottou), MIT Press, Cambridge, MA, USA, Eighteenth Annual Conference on Neural Information Processing Systems (NIPS), July 2005 (inproceedings)

**Kernel Constrained Covariance for Dependence Measurement**
In *Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics*, pages: 112-119, (Editors: R Cowell, R and Z Ghahramani), AISTATS, January 2005 (inproceedings)

**Hilbertian Metrics and Positive Definite Kernels on Probability Measures**
In *AISTATS 2005*, pages: 136-143, (Editors: Cowell, R. , Z. Ghahramani), Tenth International Workshop on Artificial Intelligence and Statistics (AI & Statistics), January 2005 (inproceedings)

**Kernel Constrained Covariance for Dependence Measurement**
AISTATS, January 2005 (talk)

**Statistical Performance of Support Vector Machines**
2004 (article)

**PAC-Bayesian Generic Chaining**
In *Advances in Neural Information Processing Systems 16*, pages: 1125-1132 , (Editors: Thrun, S., L.K. Saul, B. Schölkopf), MIT Press, Cambridge, MA, USA, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

**Hilbertian Metrics and Positive Definite Kernels on Probability Measures**
(126), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

**Advanced Lectures on Machine Learning**
*ML Summer Schools 2003*, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

**A Compression Approach to Support Vector Model Selection**
*Journal of Machine Learning Research*, 5, pages: 293-323, April 2004 (article)

**Hilbertian Metrics on Probability Measures and their Application in SVM’s**
In *Pattern Recognition, Proceedings of th 26th DAGM Symposium*, 3175, pages: 270-277, Lecture Notes in Computer Science, (Editors: Rasmussen, C. E., H. H. Bülthoff, M. Giese and B. Schölkopf), Pattern Recognition, Proceedings of th 26th DAGM Symposium, 2004 (inproceedings)

**Ranking on Data Manifolds**
In *Advances in neural information processing systems 16*, pages: 169-176, (Editors: S Thrun and L Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

**Behaviour and Convergence of the Constrained Covariance**
(130), MPI for Biological Cybernetics, 2004 (techreport)

**Kernels, Associated Structures and Generalizations**
(127), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

**Distance-Based Classification with Lipschitz Functions**
*Journal of Machine Learning Research*, 5, pages: 669-695, June 2004 (article)

**Maximal Margin Classification for Metric Spaces**
In *Learning Theory and Kernel Machines*, pages: 72-86, (Editors: Schölkopf, B. and Warmuth, M. K.), Springer, Heidelberg, Germany, 16. Annual Conference on Computational Learning Theory / COLT Kernel, 2004 (inproceedings)

**On the Convergence of Spectral Clustering on Random Samples: The Normalized Case**
In *Proceedings of the 17th Annual Conference on Learning Theory*, pages: 457-471, Proceedings of the 17th Annual Conference on Learning Theory, 2004 (inproceedings)

**Learning with Local and Global Consistency**
In *Advances in Neural Information Processing Systems 16*, pages: 321-328, (Editors: S Thrun and LK Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

**Introduction to Statistical Learning Theory**
In Lecture Notes in Artificial Intelligence 3176, pages: 169-207, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

ei
Bousquet, O.
**Introduction to Category Theory**
Internal Seminar, January 2004 (talk)

**Kernel Methods and their Potential Use in Signal Processing**
*IEEE Signal Processing Magazine*, (Special issue on Signal Processing for Mining), 2004 (article) Accepted

**Measure Based Regularization**
In *Advances in Neural Information Processing Systems 16*, pages: 1221-1228, (Editors: Thrun, S., L. Saul, B. Schölkopf), MIT Press, Cambridge, MA, USA, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

**Concentration Inequalities**
In Lecture Notes in Artificial Intelligence 3176, pages: 208-240, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

**Joint Kernel Maps**
(131), Max-Planck-Institute for Biological Cybernetics, Tübingen, November 2004 (techreport)

ei
Bousquet, O.
**Advanced Statistical Learning Theory**
Machine Learning Summer School, 2004 (talk)

**On the Complexity of Learning the Kernel Matrix**
In *Advances in Neural Information Processing Systems 15*, pages: 399-406, (Editors: Becker, S. , S. Thrun, K. Obermayer), The MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

**Ranking on Data Manifolds**
(113), Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany, June 2003 (techreport)

**A Note on Parameter Tuning for On-Line Shifting Algorithms**
Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2003 (techreport)

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

**Kernel Methods and Their Applications to Signal Processing**
In *Proceedings. (ICASSP ‘03)*, Special Session on Kernel Methods, pages: 860 , ICASSP, 2003 (inproceedings)

**Learning with Local and Global Consistency**
(112), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, June 2003 (techreport)

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

**New Approaches to Statistical Learning Theory**
*Annals of the Institute of Statistical Mathematics*, 55(2):371-389, 2003 (article)

**Distance-based classification with Lipschitz functions**
In *Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory*, pages: 314-328, (Editors: Schölkopf, B. and M.K. Warmuth), Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory, 2003 (inproceedings)

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

**Concentration Inequalities for Sub-Additive Functions Using the Entropy Method**
*Stochastic Inequalities and Applications*, 56, pages: 213-247, Progress in Probability, (Editors: Giné, E., C. Houdré and D. Nualart), November 2003 (article)

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