Acquisition of Appropriate Bias for Inductive Concept Learning

Paul E. Utgoff, Tom M. Mitchell

Current approaches to inductive concept learning suffer from a fundamental difficulty; if a fixed language is chosen in which to represent concepts, then in cases where that language is inappropriate, the new concept may be impossible to describe (and therefore to learn). We suggest a framework for automatically extending the language in which concepts are to be expressed. This framework includes multiple sources of knowledge for recommending plausible language extensions.


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