Machine Learning Department
School of Computer Science, Carnegie Mellon University


Theoretical Foundations of Active Learning

Steve Hanneke

May 2009

Ph.D. Thesis


Keywords: Active learning, statistical learning theory, sequential design, selective sampling

I study the informational complexity of active learning in a statistical learning theory framework. Specifically, I derive bounds on the rates of convergence achievable by active learning, under various noise models and under general conditions on the hypothesis class. I also study the theoretical advantages of active learning over passive learning, and develop procedures for transforming passive learning algorithms into active learning algorithms with asymptotically superior label complexity. Finally, I study generalizations of active learning to more general forms of interactive statistical learning.

160 pages

SCS Technical Report Collection
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