Lazy Learning

This edited collection describes recent progress on lazy learning, a branch of machine learning concerning algorithms that defer the processing of their inputs, reply to information requests by combining stored data, and typically discard constructed replies. It is the first edited volume in AI on this topic, whose many synonyms include instance-based', memory-based'. exemplar-based', and local learning', and whose topic intersects case-based reasoning and edited k-nearest neighbor classifiers. It is intended for AI researchers and students interested in pursuing recent progress in this branch of machine learning, but, due to the breadth of its contributions, it should also interest researchers and practitioners of data mining, case-based reasoning, statistics, and pattern recognition.

Author: David W. AHA
Publisher: Springer
Published: 05/31/1997
Pages: 424
Binding Type: Hardcover
Weight: 1.71lbs
Size: 9.21h x 6.14w x 0.94d
ISBN: 9780792345848

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