Matrices h
Margins, support vectors, and linear programming, oh my! Reading: Bishop, 4.0, 4.1, 7.0, 7.1 Burges tutorial (on class resources page)
Linear Methods, cont’d; SVMs intro. Straw poll Which would you rather do first? Unsupervised learning Clustering Structure of data Scientific discovery.
Arizona State University DMML Kernel Methods – Gaussian Processes Presented by Shankar Bhargav.
Learning a Kernel Matrix for Nonlinear Dimensionality Reduction By K. Weinberger, F. Sha, and L. Saul Presented by Michael Barnathan.
GRASP Learning a Kernel Matrix for Nonlinear Dimensionality Reduction Kilian Q. Weinberger, Fei Sha and Lawrence K. Saul ICML’04 Department of Computer.
Margins, support vectors, and linear programming Thanks to Terran Lane and S. Dreiseitl.
Lectures on Orbifolds
LMS Algorithm in a Reproducing Kernel Hilbert Space
Learning a Kernel Matrix for Nonlinear Dimensionality Reduction
Support Vector Machines Part 2
1 Multiple Kernel Learning Naouel Baili MRL Seminar, Fall 2009.
