Oversearching and Layered Search in Empirical Learning

Oversearching and Layered Search in Empirical Learning (1995)

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by J. R. Quinlan , R. M. Cameron-jones

Venue:	In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence
Citations:	76 - 0 self

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Datum	Value	Source
TITLE	Oversearching and Layered Search in Empirical Learning	user correction - Legacy Corrections
AUTHOR NAME	J. R. Quinlan	SVM HeaderParse 0.1
AUTHOR AFFIL	Basser Department of Computer Science; University of Sydney	SVM HeaderParse 0.2
AUTHOR ADDR	Sydney 2006; Australia	SVM HeaderParse 0.1
AUTHOR NAME	R. M. Cameron-jones	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Applied Computing; University of Tasmania	SVM HeaderParse 0.2
AUTHOR ADDR	Launceston 7250; Australia	SVM HeaderParse 0.1
ABSTRACT	When learning classifiers, more extensive search for rules is shown to lead to lower predictive accuracy on many of the real-world domains investigated. This counter-intuitive result is particularly relevant to recent systematic search methods that use risk-free pruning to achieve the same outcome as exhaustive search. We propose an iterated search method that commences with greedy search, extending its scope at each iteration until a stopping criterion is satisfied. This layered search is often found to produce theories that are more accurate than those obtained with either greedy search or moderately extensive beam search. 1 Introduction Mitchell [1982] observes that the generalization implicit in learning from examples can be viewed as a search over the space of possible theories. From this perspective, most machine learning methods carry out a series of local searches in the vicinity of the current theory, selecting at each step the most promising improvement. Covering algorithms ...	user correction - Legacy Corrections
YEAR	1995	INFERENCE
VENUE	In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	1019--1024	INFERENCE
CITATIONS	13 found	ParsCit 1.0