[Lisa_seminaires] REMINDER: UdeM-McGill-MITACS machine learning seminar Tue May 27th, 10:00am, MC437

Dumitru Erhan erhandum at IRO.UMontreal.CA
Lun 26 Mai 10:02:47 EDT 2008


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.


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