What works well tells us what works better

Bensusan Hilan, Giraud-Carrier Christophe, Pfahringer Bernhard

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

Abstract

We have now a large number of learning algorithms available. What works well where? In order to find correlations between areas of expertise of learning algorithms and learning tasks, we can resort to \em meta-learning. Several meta-learning scenarios have been proposed. In one scenario, we are searching for the best learning algorithm for a problem. The decision can be made using different strategies. In any approach to meta-learning, it is crucial to choose relevant features to describe a task. Different strategies of task description have been proposed: some strategies based on statistical features of the dataset, some based on information-theoretic properties, others based on a learning algorithm's representation of the task. In this work we present a novel approach to task description, called landmarking.
Translated title of the contributionWhat works well tells us what works better
Original languageEnglish
Title of host publicationProceedings of ICML'2000 workshop on What Works Well Where
PublisherICML'2000
Publication statusPublished - 2000

Bibliographical note

Other page information: 1-8
Conference Proceedings/Title of Journal: Proceedings of ICML'2000 workshop on What Works Well Where
Other identifier: 1000469

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