Lazy Learning (Paperback, Softcover reprint of the original 1st ed. 1997)

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.

General

Imprint:	Springer
Country of origin:	Netherlands
Release date:	December 2010
First published:	1997
Editors:	David W. Aha
Dimensions:	279 x 210 x 22mm (L x W x T)
Format:	Paperback
Pages:	424
Edition:	Softcover reprint of the original 1st ed. 1997
ISBN-13:	978-90-481-4860-8
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	90-481-4860-X
Barcode:	9789048148608

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