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Harmonia loosely praestabilita: discovering adequate inductive strategies

Hilan Bensusan, Christophe Giraud-Carrier, Harmonia loosely praestabilita: discovering adequate inductive strategies. Proceedings of the 22nd Annual Meeting of the Cognitive Science Society, pp. 609–614. August 2000. PDF, 153 Kbytes.

Abstract

Landmarking is a novel approach to inductive model selection in Machine Learning. It uses simple, bare-bone inductive strategies to describe tasks and induce correlations between tasks and strategies. The paper presents the technique and reports experiments showing that landmarking performs well in a number of different scenarios. It also discusses the implications of landmarking to our understanding of inductive refinement.

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