Advanced Statistical Learning Theory
published: Feb. 25, 2007, recorded: September 2004, views: 10764
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
This set of lectures will complement the statistical learning theory course and focus on recent advances in the domain of classification. 1- PAC Bayesian bounds: a simple derivation, comparison with Rademacher averages.
2 - Local Rademacher complexity with classification loss, Talagrand's inequality. Tsybakov noise conditions.
3 - Properties of loss functions for classification (influence on approximation and estimation, relationship with noise conditions).
4 - Applications to SVM - Estimation and approximation properties, role of eigenvalues of the Gram matrix.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !