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
Smola, Schölkopf, Müller - Kernels and Regularization
Aronszajn - RKHS paper (the one that started it all)
Schölkopf, Herbrich, Smola - Generalized Representer Theorem
Hofmann, Scholkopf, Smola - Kernel Methods in Machine Learning
Teo, Globerson, Roweis and Smola - Convex learning with Invariances
Caetano, McAuley, Le, Smola - Learning Graph Matching
Keshet and McAllester - Tighter bounds for ramp loss
Chapelle, Do, Le, Smola, Teo - Ramp loss examples
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).
Page generated 2012-03-15 16:26:47 PDT, by jemdoc.