Theoretical Foundations of Active Learning
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2009
- Accession Number
- ADA501773
Entities
People
- Steve Hanneke
Organizations
- Carnegie Mellon University