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.
Descriptors:
Subject Categories:
- Psychology