Learning Fallible Deterministic Finite Automata |
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Authors: | Ron Dana Rubinfeld Ronitt |
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Affiliation: | (1) Computer Science Institute, Hebrew University, 91904 Jerusalem, Israel;(2) Computer Science Department, Cornell University, 14853 Ithaca, NY, USA |
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Abstract: | We consider the problem of learning from a fallible expert that answers all queries about a concept, but often gives incorrect answers. The expert can also be thought of as a truth table describing the concept which has been partially corrupted. In order to learn the underlying concept with arbitrarily high precision, we would like to use its structure in order to correct most of the incorrect answers. We assume that the expert's errors are uniformly and independently distributed, occur with any fixed probability strictly smaller than 1/2, and are persistent. In particular, we present a polynomial time algorithm using membership queries for correcting and learning fallible Deterministic Finite Automata under the uniform distribution. |
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Keywords: | PAC Learning under the Uniform Distribution Persistent Errors Fallible Expert Deterministic Finite Automata |
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