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) : Hanneke, Steve


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a501773.pdf


Report Date : May 2009


Pagination or Media Count : 160


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 :   *LEARNING , THEORY , CONVERGENCE , STATISTICAL ANALYSIS , ALGORITHMS , THESES


Subject Categories : Psychology


Distribution Statement : APPROVED FOR PUBLIC RELEASE