Version-space induction with multiple concept languages
The version space approach suffers from two main problems, i. e. inability of inducing concepts consistent with data due to use of a restricted hypothesis space and lack of computational efficiency. In this paper we investigate the use of multiple concept languages in a version space approach. We define a graph of languages ordered by the size of their associated concept sets, and provide a procedure for efficiently inducing version spaces while shifting from small to larger concept languages. We show how this framework can help overcome the two above-mentioned limitations. Also, compared to other work on language shift, our approach suggests an alternative strategy for searching the space of new concepts, which is not based on constructive operators.
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