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