Sampling-Based Relative Landmarks: Systematically Test-Driving Algorithms Before Choosing
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
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Task description is crucial not only to every meta-learning enterprise but also to related endeavours like transfer of learning. This paper evaluates the performance of a newly introduced method of task description, landmarking, in a supervised meta-learning scenario. The method relies on correlations between simple and more sophisticated learning algorithms to select the best learner for a task. The results compare favourably with an information-based method and suggest that landmarking holds promise.