SML: Kernels

STATISTICS 241B, COMPUTER SCIENCE 281B

Content

Kernels
- Hilbert Spaces
- Regularization theory
- Kernels on strings, sets, graphs, images
Efficient algorithms
- Dual space (using α)
- Reduced dimensionality (low rank expanions)
- Function space (using fast Kα)
- Primal space (hashing & random kitchen sinks)
Structured estimation
- Sequence annotation and segmentation
- Ranking and graph matching
- Ramp loss, consistency, and invariances

Supplementary material

Slides in PDF and Keynote. If you want to extract the equations from the slides you can do so by using LaTeXit, simply by dragging the equation images into it. There's also an optimization chapter from the Learning with Kernels book.

Girosi - Equivalence between sparse approximation and SVM PDF
Smola, Schölkopf, Müller - Kernels and Regularization PDF
Aronszajn - RKHS paper (the one that started it all) link
Schölkopf, Herbrich, Smola - Generalized Representer Theorem PDF
Hofmann, Scholkopf, Smola - Kernel Methods in Machine Learning PDF
Teo, Globerson, Roweis and Smola - Convex learning with Invariances PDF
Caetano, McAuley, Le, Smola - Learning Graph Matching PDF
Keshet and McAllester - Tighter bounds for ramp loss PDF
Chapelle, Do, Le, Smola, Teo - Ramp loss examples PDF
Platt - Sequential Minimal Optimization PDF
Joachims - Multivariate performance measures link

Videos

This is unedited video straight from a Lumix GF2 with a 20mm lens which should explain the sound (it doesn't have a dedicated audio input) … But it should help as a supplement with the slides (YouTube typically makes the 1080i version available within 1 week of the upload).