SML: Kernels



  • 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


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).