Quantifying the Inductive Bias in Concept Learning (extended abstract)

David Haussler

We show that the notion of bias in inductive concept learning can be quantified in a way that directly relates to learning performance, and that this quantitative theory of bias can provide guidance in the design of effective learning algorithms. We apply this idea by measuring some common language biases, including restriction to conjunctive concepts and conjunctive concepts with internal disjunction, and, guided by these measurements, develop learning algorithms for these classes of concepts that have provably good convergence properties.


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