Publication
ICML 2009
Conference paper

Importance weighted active learning

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Abstract

We present a practical and statistically consistent scheme for actively learning binary classifiers under general loss functions. Our algorithm uses importance weighting to correct sampling bias, and by controlling the variance, we are able to give rigorous label complexity bounds for the learning process. Copyright 2009.

Date

Publication

ICML 2009

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