Statistics – Machine Learning
Scientific paper
2011-11-08
Statistics
Machine Learning
20 pages, 4 figures, 2 tables, a few minor typos were corrected
Scientific paper
In this paper we address the problem of pool based active learning, and provide an algorithm, called UPAL, that works by minimizing the unbiased estimator of the risk of a hypothesis in a given hypothesis space. For the space of linear classifiers and the squared loss we show that UPAL is equivalent to an exponentially weighted average forecaster. Exploiting some recent results regarding the spectra of random matrices allows us to establish consistency of UPAL when the true hypothesis is a linear hypothesis. Empirical comparison with an active learner implementation in Vowpal Wabbit, and a previously proposed pool based active learner implementation show good empirical performance and better scalability.
Ganti Ravi
Gray Alexander
No associations
LandOfFree
UPAL: Unbiased Pool Based Active Learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with UPAL: Unbiased Pool Based Active Learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and UPAL: Unbiased Pool Based Active Learning will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-52880