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We introduce a new model for learning with membership queries in which queries near the boundary of a target concept may receive incorrect or "don't care" responses. In partial compensation, we assume the distribution of examples has zero probability mass on the boundary region. The motivation behind this model is that the reason for the incorrect (or "don't care") response is that these examples are extremely rare in practice. Thus, it does not matter how the learner classifies them. Wedoi:10.1145/225298.225310 dblp:conf/colt/BlumCGS95 fatcat:7vhgbr3vdbfv7mvmg22ir5igvm