ComplexityBased Induction (1994)
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BibTeX
@MISC{Conklin94complexitybasedinduction,
author = {Darrell Conklin Conklin and Ian H. Witten and J. R. Quinlan},
title = {ComplexityBased Induction},
year = {1994}
}
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Abstract
. A central problem in inductive logic programming is theory evaluation. Without some sort of preference criterion, anytwo theories that explain a set of examples are equally acceptable. This paper presents a scheme for evaluating alternative inductive theories based on an objective preference criterion. It strives to extract maximal redundancy from examples, transforming structure into randomness. A major strength of the method is its application to learning problems where negative examples of concepts are scarce or unavailable. A new measure called model complexity is introduced, and its use is illustrated and compared with a proof complexity measure on relational learning tasks. The complementarity of model and proof complexity parallels that of model and proof#theoretic semantics. Model complexity, where applicable, seems to be an appropriate measure for evaluating inductive logic theories. Keywords: Inductive logic programming, data compression, minimum description length princip...
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