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IMA, March 12, 2003 Gert Lanckriet ( gert@eecs.berkeley )

Learning the Kernel Matrix with Semi-Definite Programming. IMA, March 12, 2003 Gert Lanckriet ( gert@eecs.berkeley.edu ) Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, Michael Jordan U.C. Berkeley. Learning the Kernel Matrix with SDP. Machine learning. Kernel-based machine learning.

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IMA, March 12, 2003 Gert Lanckriet ( gert@eecs.berkeley )

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  1. Learning the Kernel Matrix with Semi-Definite Programming IMA, March 12, 2003 Gert Lanckriet (gert@eecs.berkeley.edu) Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, Michael Jordan U.C. Berkeley

  2. Learning the Kernel Matrix with SDP

  3. Machine learning Kernel-based machine learning Introduction: the idea (1)

  4. Introduction: the idea (2)

  5. Introduction: the idea (3)

  6. training set (labelled) test set (unlabelled) Introduction: the idea (4)

  7. Introduction: the idea (5)

  8. Learning the Kernel Matrix with SDP

  9. Semi-Definite Programming (SDP)

  10. SDP - hard margin SVM classifiers (1)

  11. SDP - hard margin SVM classifiers (2)

  12. SDP - hard margin SVM classifiers (3)

  13. SDP - hard margin SVM classifiers (4)

  14. SDP - hard margin SVM classifiers (5)

  15. SDP - hard margin SVM classifiers (6)

  16. Reminder: Schur complement lemma

  17. SDP - hard margin SVM classifiers (7)

  18. SDP - hard margin SVM classifiers (8) SDP !

  19. Learning the Kernel Matrix with SDP

  20. Learning LKM - hard margin SVM classifiers (1) Optimization Learning the kernel matrix !

  21. LKM - hard margin SVM classifiers (2) training set (labelled) test set (unlabelled) Learning the kernel matrix !

  22. LKM - hard margin SVM classifiers (3) ?

  23. LKM - hard margin SVM classifiers (4)

  24. LKM - hard margin SVM classifiers (4)

  25. LKM - hard margin SVM classifiers (4)

  26. LKM - hard margin SVM classifiers (5)

  27. LKM - hard margin SVM classifiers (6)

  28. LKM - hard margin SVM classifiers (7) Learning Kernel Matrix with SDP !

  29. Learning the Kernel Matrix with SDP

  30. Empirical results hard margin SVMs

  31. Learning the Kernel Matrix with SDP

  32. Conclusions and future directions

  33. Conclusions and future directions

  34. See also

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