Nonfiction | Computer & Technology

Lazy Learning

by David W. Aha

2020-09-19 14:39:03

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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... Read more
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. Less

Book Details

File size9.25 X 6.1 X 0 in
Print pages427
PublisherSpringer Netherlands
Publication date May 31, 1997
LanguageEnglish
ISBN9789401720533
autor-image
David W. Aha
Author
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