Oversearching and Layered Search in Empirical Learning
نویسندگان
چکیده
When learning classiiers, more extensive search for rules is shown to lead to lower pre-dictive 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 satissed. 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.
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تاریخ انتشار 1995