This week's UdeM-McGill-MITACS seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en ):

Learning in the Sample Compression Framework

by Mohak Shah, Probabilistic Vision Group Centre for Intelligent Machines McGill University

Location: McConnell Engineering (McGill), room 437 Time: May 27th 2008, 10am

This talk will focus on the algorithms’ learning capabilities under the Sample Compression framework highlighting some of the advantages over other learning paradigms. We will show how theoretically motivated algorithms with practical realizable generalization error bounds can be obtained and further how these risk bounds can be utilized to guide the model selection. As a special case, we will focus on decision based classifiers (e.g. learning conjunctions or disjunctions of data-dependent features) and show how performances comparable to and in some cases better than the state-of-the-art learning algorithms such as SVM can be obtained.