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Olivier Chapelle: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Adam Kowalczyk, Olivier Chapelle
    An Analysis of the Anti-learning Phenomenon for the Class Symmetric Polyhedron. [Citation Graph (0, 0)][DBLP]
    ALT, 2005, pp:78-91 [Conf]
  2. Olivier Chapelle, Mingmin Chi, Alexander Zien
    A continuation method for semi-supervised SVMs. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:185-192 [Conf]
  3. Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle
    Deterministic annealing for semi-supervised kernel machines. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:841-848 [Conf]
  4. Christian Walder, Olivier Chapelle, Bernhard Schölkopf
    Implicit surface modelling as an eigenvalue problem. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:936-939 [Conf]
  5. Holger Fröhlich, Olivier Chapelle, Bernhard Schölkopf
    Feature Selection for Support Vector Machines by Means of Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    ICTAI, 2003, pp:142-148 [Conf]
  6. Gavin C. Cawley, Nicola L. C. Talbot, Olivier Chapelle
    Estimating Predictive Variances with Kernel Ridge Regression. [Citation Graph (0, 0)][DBLP]
    MLCW, 2005, pp:56-77 [Conf]
  7. Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc J. Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-Taylor, Amos J. Storkey, Sándor Szedmák, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang
    The 2005 PASCAL Visual Object Classes Challenge. [Citation Graph (0, 0)][DBLP]
    MLCW, 2005, pp:117-176 [Conf]
  8. Olivier Bousquet, Olivier Chapelle, Matthias Hein
    Measure Based Regularization. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  9. Olivier Chapelle, Zaid Harchaoui
    A Machine Learning Approach to Conjoint Analysis. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  10. Olivier Chapelle, Bernhard Schölkopf
    Incorporating Invariances in Non-Linear Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:609-616 [Conf]
  11. Olivier Chapelle, Vladimir Vapnik
    Model Selection for Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:230-236 [Conf]
  12. Olivier Chapelle, Vladimir Vapnik, Jason Weston
    Transductive Inference for Estimating Values of Functions. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:421-427 [Conf]
  13. Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik
    Vicinal Risk Minimization. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:416-422 [Conf]
  14. Olivier Chapelle, Jason Weston, Bernhard Schölkopf
    Cluster Kernels for Semi-Supervised Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:585-592 [Conf]
  15. Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik
    Kernel Dependency Estimation. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:873-880 [Conf]
  16. Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso Poggio, Vladimir Vapnik
    Feature Selection for SVMs. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:668-674 [Conf]
  17. Jason Weston, Fernando Pérez-Cruz, Olivier Bousquet, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf
    Feature selection and transduction for prediction of molecular bioactivity for drug design. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2003, v:19, n:6, pp:764-771 [Journal]
  18. Holger Fröhlich, Olivier Chapelle, Bernhard Schölkopf
    Feature Selection for Support Vector Machines Using Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    International Journal on Artificial Intelligence Tools, 2004, v:13, n:4, pp:791-800 [Journal]
  19. S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste
    Building Support Vector Machines with Reduced Classifier Complexity. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:1493-1515 [Journal]
  20. Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio
    Model Selection for Small Sample Regression. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:48, n:1-3, pp:9-23 [Journal]
  21. Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee
    Choosing Multiple Parameters for Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:131-159 [Journal]
  22. Vladimir Vapnik, Olivier Chapelle
    Bounds on Error Expectation for Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:9, pp:2013-2036 [Journal]
  23. Christian Walder, Bernhard Schölkopf, Olivier Chapelle
    Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:273-280 [Conf]
  24. S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle
    An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:673-680 [Conf]
  25. Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi
    Branch and Bound for Semi-Supervised Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:217-224 [Conf]

  26. Supervised semantic indexing. [Citation Graph (, )][DBLP]


  27. Expected reciprocal rank for graded relevance. [Citation Graph (, )][DBLP]


  28. Multi-task learning for boosting with application to web search ranking. [Citation Graph (, )][DBLP]


  29. Learning with Transformation Invariant Kernels. [Citation Graph (, )][DBLP]


  30. An Analysis of Inference with the Universum. [Citation Graph (, )][DBLP]


  31. A General Boosting Method and its Application to Learning Ranking Functions for Web Search. [Citation Graph (, )][DBLP]


  32. Tighter Bounds for Structured Estimation. [Citation Graph (, )][DBLP]


  33. Large Margin Taxonomy Embedding for Document Categorization. [Citation Graph (, )][DBLP]


  34. Global ranking by exploiting user clicks. [Citation Graph (, )][DBLP]


  35. Active learning for ranking through expected loss optimization. [Citation Graph (, )][DBLP]


  36. Learning more powerful test statistics for click-based retrieval evaluation. [Citation Graph (, )][DBLP]


  37. A dynamic bayesian network click model for web search ranking. [Citation Graph (, )][DBLP]


  38. Web spam identification through content and hyperlinks. [Citation Graph (, )][DBLP]


  39. Early exit optimizations for additive machine learned ranking systems. [Citation Graph (, )][DBLP]


  40. Implicit Surface Modelling with a Globally Regularised Basis of Compact Support. [Citation Graph (, )][DBLP]


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