Date of Original Version

4-2011

Type

Conference Proceeding

Journal Title

Journal of Machine Learning Research : Workshop and Conference Proceedings

Volume

15

Rights Management

Copyright 2011 by the authors.

Abstract or Description

We prove that access to a prior distribution over target functions can dramatically improve the sample complexity of self-terminating active learning algorithms, so that it is always better than the known results for prior-dependent passive learning. In particular, this is in stark contrast to the analysis of prior-independent algorithms, where there are simple known learning problems for which no self-terminating algorithm can provide this guarantee for all priors

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Published In

Journal of Machine Learning Research : Workshop and Conference Proceedings, 15.