Carnegie Mellon University
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The Sample Complexity of Self-Verifying Bayesian Active Learning

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journal contribution
posted on 2011-04-01, 00:00 authored by Liu Yang, Steve Hanneke, Jaime G. Carbonell

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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Copyright 2011 by the authors.



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