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A higher-order approach to meta-learning

Hilan Bensusan, Christophe Giraud-Carrier, Claire Kennedy, A higher-order approach to meta-learning. Proceedings of the ECML'2000 workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, pp. 109–117. June 2000. PDF, 172 Kbytes.


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.

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