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ML Lunch (Feb 3, 2014): Learning with a Drifting Target Concept

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Published on Feb 3, 2014

Speaker: Liu Yang
School of Computer Science, CMU

Abstract
We study the problem of learning in the presence of a drifting target concept. Specifically, we provide bounds on the expected number of mistakes on a sequence of i.i.d. points, labeled according to a target concept that can change by a given amount on each round. Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the number of mistakes.

This is joint work with Steve Hanneke and Varun Kanade.

For more ML Lunch talks, visit http://www.cs.cmu.edu/~learning/

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