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The Sample Complexity of Learning from Carefully Chosen Examples
Speaker: Dr. Sandra Zilles, Dept of Computer Science, University of Regina
Date: Friday, February 12, 2010
Time: 3:30 pm
Place: Thorvaldson 124
*** Everyone Welcome ***
For more information on this and other seminars in the Department Seminar Series, please see the Department Seminar Series page.
Abstract
Most machine learning models assume either (a) that the data fed to a learning algorithm is sampled at random from some (unknown) distribution or (b) that a learning algorithm has to be successful even for the worst possible data presentation (no matter how unlikely it is). However, in many application scenarios, e.g., when a human interacts with a learning machine, in fact "helpful" data is selected and presented to the learning machine by a "teacher".
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