Accession Number:

ADA501773

Title:

Theoretical Foundations of Active Learning

Descriptive Note:

Doctoral thesis

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA MACHINE LEARNING DEPT

Personal Author(s):

Report Date:

2009-05-01

Pagination or Media Count:

160.0

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.

Subject Categories:

  • Psychology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE