Active data labeling for improved classifier generalizability
Abstract
Existing statistical learning methods perform well when evaluated on training and test data drawn from the same distribution. In practice, however, these distributions are not always the same. In this paper we derive an estimable upper bound on the test error rate that depends on a new probability distance measure between training and test distributions. Furthermore, we identify a non-parametric estimator for this distance measure that can be...
Paper Details
Title
Active data labeling for improved classifier generalizability
Published Date
Mar 1, 2015
Journal
Volume
108
Pages
272 - 277
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