Harmonia loosely praestabilita: discovering adequate inductive strategies

Hilan Bensusan, Christophe Giraud-Carrier

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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.
Translated title of the contributionHarmonia loosely praestabilita: discovering adequate inductive strategies
Original languageEnglish
Title of host publicationProceedings of the 22nd Annual Meeting of the Cognitive Science Society
PublisherCognitive Science Society
Pages609 - 614
Number of pages5
Publication statusPublished - 2000

Bibliographical note

Other page information: 609-614
Conference Proceedings/Title of Journal: Proceedings of the 22nd Annual Meeting of the Cognitive Science Society
Other identifier: 1000501

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