Material Detail

Advanced Statistical Learning Theory

This video was recorded at Machine Learning Summer School (MLSS), Berder Island 2004. 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.

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science / Programming & Programming Languages

Go to Material

Bookmark / Add to Course ePortfolio

Create a Learning Exercise

Add Accessibility Information

Rate

Add a Comment

Quality

User Rating
Comments
Learning Exercises
Bookmark Collections
Course ePortfolios
Accessibility Info

Report Broken Link
Report as Inappropriate

More about this material

Material Type:: Presentation
Date Added to MERLOT:: February 10, 2015
Date Modified in MERLOT:: February 10, 2015
Author:: Olivier Bousquet, Google
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Accessibility Information Available:: Unknown
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States