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 contribution | A higher-order approach to meta-learning |
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Original language | English |
Title of host publication | Proceedings of the ECML'2000 workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination |
Publisher | ECML'2000 |
Pages | 109 - 117 |
Number of pages | 8 |
Publication status | Published - 2000 |
Bibliographical note
Other page information: 109-117Conference 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