Lazy Learning - Brossura

Brossura

ISBN 10: 904814860X ISBN 13: 9789048148608

Casa editrice: Springer, 2010

Riassunto

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.

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Contenuti

Editorial; D.W. Aha. Locally Weighted Learning; C.G. Atkeson, et al. Locally Weighted Learning for Control; C.G. Atkeson, et al. Voting over Multiple Condensed Nearest Neighbors; E. Alpaydin. Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching; M. Salganicoff. Discretisation in Lazy Learning Algorithms; Kai Ming Ting. Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms; Jianping Zhang, et al. The Racing Algorithm: Model Selection for Lazy Learners; O. Maron, A.W. Moore. Context-Sensitive Feature Selection for Lazy Learners; P. Domingos. Computing Optimal Attribute Weight Settings for Nearest Neighbor Algorithms; C.X. Ling, Handong Wang. A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms; D. Wettschereck, et al. Lazy Acquisition of Place Knowledge; P. Langley, et al. A Teaching Strategy for Memory-Based Control; J.W. Sheppard, S.L. Salzberg. Lazy Incremental Learning of Control Knowledge for Efficiently Obtaining Quality Plans; D. Borrajo, M. Veloso. IGTree: Using Trees for Compression and Classification in Lazy Learning Algorithms; W. Daelemans, et al.

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 904814860X
• ISBN 13: 9789048148608
• Rilegatura: Copertina flessibile
• Numero di pagine: 432
• Redattore: Aha David W.
• Contatto del produttore: non disponibile
• Persona responsabile: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Germania