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Tijl De Bie (K.U.Leuven)

Optimal Experiment Design (OED) for kernel ridge regression and the Minimum Volume Covering Ellipsoid (MVCE). Tijl De Bie (K.U.Leuven). Joint work with: Alexander Dolia John Shawe-Taylor Michael Titterington Chris Harris. The next hour…. Optimal experiment design?. OED?

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Tijl De Bie (K.U.Leuven)

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  1. Optimal Experiment Design (OED)for kernel ridge regressionandthe Minimum Volume Covering Ellipsoid (MVCE) Tijl De Bie (K.U.Leuven) Joint work with: Alexander Dolia John Shawe-Taylor Michael Titterington Chris Harris

  2. The next hour… Tijl De Bie - KULeuven

  3. Optimal experiment design? OED? Notation Examples Deliverables… Tijl De Bie - KULeuven

  4. Optimal experiment design? OED? Notation Examples Deliverables… Tijl De Bie - KULeuven

  5. Optimal experiment design? OED? Notation Examples Deliverables… Tijl De Bie - KULeuven

  6. Notation OED? Notation Examples Deliverables… Tijl De Bie - KULeuven

  7. Examples OED? Notation Examples Deliverables… Tijl De Bie - KULeuven

  8. Examples OED? Notation Examples Deliverables… Tijl De Bie - KULeuven

  9. Deliverables in this talk… OED? Notation Examples Deliverables… Tijl De Bie - KULeuven

  10. Least squares regression (LS) Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  11. Least squares regression (LS) Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  12. Optimal experiment design for LS Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  13. Optimal experiment design for LS Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  14. Optimal experiment design for LS Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  15. Optimal experiment design for LS Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  16. Ridge regression (RR) Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  17. Ridge regression (RR) Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  18. Ridge regression (RR) Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  19. Optimal experiment design for RR Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  20. Optimal experiment design for RR Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  21. Optimal experiment design for RR Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  22. Kernel ridge regression (KRR) Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  23. Kernel ridge regression (KRR) Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  24. Kernel ridge regression (KRR) Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  25. Kernel D-OED Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  26. Kernel D-OED Least squares Ridge regression Kernel RR Tijl De Bie - KULeuven

  27. OED: summary D-OED MVCE standard Least squares Ridge regression Kernel RR regularized kernel Tijl De Bie - KULeuven

  28. Now over to novelty detection! Novelty detection MVCE and duality Regularized MVCE Kernel MVCE Tijl De Bie - KULeuven

  29. Now over to novelty detection! Novelty detection MVCE and duality Regularized MVCE Kernel MVCE Tijl De Bie - KULeuven

  30. Regularized MVCE Novelty detection MVCE and duality Regularized MVCE Kernel MVCE Tijl De Bie - KULeuven

  31. Kernel MVCE Novelty detection MVCE and duality Regularized MVCE Kernel MVCE Tijl De Bie - KULeuven

  32. Kernel MVCE Novelty detection MVCE and duality Regularized MVCE Kernel MVCE Tijl De Bie - KULeuven

  33. Kernel MVCE Novelty detection MVCE and duality Regularized MVCE Kernel MVCE Tijl De Bie - KULeuven

  34. MVCE: summary Novelty detection MVCE and duality Regularized MVCE Kernel MVCE Tijl De Bie - KULeuven

  35. MVCE: summary D-OED MVCE standard regularized Novelty detection MVCE and duality Regularized MVCE Kernel MVCE kernel Tijl De Bie - KULeuven

  36. MVCE: dealing with outliers Novelty detection MVCE and duality Regularized MVCE Kernel MVCE Tijl De Bie - KULeuven

  37. Experiments – MVCE Linear Tijl De Bie - KULeuven

  38. Experiments – MVCE Linear, centered Tijl De Bie - KULeuven

  39. Experiments – MVCE RBF-kernel Tijl De Bie - KULeuven

  40. Experiments – MVCE RBF-kernel Soft-margin Tijl De Bie - KULeuven

  41. Experiments – D-OED Tijl De Bie - KULeuven

  42. Experiments – D-OED Costs: Random vs Uniform vs OED (blue) 2-norm infinity norm 1-norm Tijl De Bie - KULeuven

  43. Conclusions Tijl De Bie - KULeuven

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