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2000
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Meta-Learning by Landmarking Various Learning Algorithms

11 years 5 months ago
Meta-Learning by Landmarking Various Learning Algorithms
Landmarking is a novel approach to describing tasks in meta-learning. Previous approaches to meta-learning mostly considered only statistics-inspired measures of the data as a source for the definition of metaattributes. Contrary to such approaches, landmarking tries to determine the location of a specific learning problem in the space of all learning problems by directly measuring the performance of some simple and efficient learning algorithms themselves. In the experiments reported we show how such a use of landmark values can help to distinguish between areas of the learning space favouring different learners. Experiments, both with artificial and real-world databases, show that landmarking selects, with moderate but reasonable level of success, the best performing of a set of learning algorithms.
Bernhard Pfahringer, Hilan Bensusan, Christophe G.
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2000
Where ICML
Authors Bernhard Pfahringer, Hilan Bensusan, Christophe G. Giraud-Carrier
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