Selective Sampling with Co-Testing: Preliminary Results

Ion Muslea, Steven Minton, and Craig A. Knoblock, University of Southern California

We present a novel approach to selective sampling, co-testing, which can be applied to problems with redundant views (i.e., problems with multiple disjoint sets of attributes that can be used for learning). The main idea behind co-testing consists of selecting the queries among the unlabeled examples on which the existing views disagree.

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