A higher-order approach to meta-learning

Hilan Bensusan, Christophe Giraud-Carrier, Claire Kennedy

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

Abstract

Meta-learning, as applied to model selection, consists of inducing mappings from tasks to learners. Traditionally, tasks are characterised by the values of pre-computed meta-attributes, such as statistical and information-theoretic measures, induced decision trees' characteristics and/or landmarkers' performances. In this position paper, we propose to (meta-)learn directly from induced decision trees, rather than rely on an \em ad hoc set of pre-computed characteristics. Such meta-learning is possible within the framework of the typed higher-order inductive learning framework we have developed.
Translated title of the contributionA higher-order approach to meta-learning
Original languageEnglish
Title of host publicationProceedings of the ECML'2000 workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination
PublisherECML'2000
Pages109 - 117
Number of pages8
Publication statusPublished - 2000

Bibliographical note

Other page information: 109-117
Conference Proceedings/Title of Journal: Proceedings of the ECML'2000 workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination
Other identifier: 1000471

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