1 / 43

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?

natala
Télécharger la présentation

Tijl De Bie (K.U.Leuven)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related