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Online Learning and Bregman Divergences

This video was recorded at Machine Learning Summer School (MLSS), Taipei 2006. L 1: Introduction to Online Learning (Predicting as good as the best expert, Predicting as good as the best linear combination of experts, Additive versus multiplicative family of updates) L 2: Bregman divergences and Loss bounds (Introduction to Bregman divergences, Relative loss bounds for the linear case, Nonlinear case & matching losses, Duality and relation to exponential families) L 3: Extensions, interpretations, applications (Online to Batch Conversions, Prior information on the weight vector, Some applications)

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science / Programming & Programming Languages

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Material Type:: Presentation
Date Added to MERLOT:: February 10, 2015
Date Modified in MERLOT:: February 10, 2015
Author:: Manfred K. Warmuth, Computer Science, UC Santa Cruz
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
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States