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OverviewFull Product DetailsAuthor: David W. Aha , David W. AhaPublisher: Springer Imprint: Springer Edition: Reprinted from ARTIFICIAL INTELLIGENCE REVIEW, 11:1-5, 1997 Dimensions: Width: 15.50cm , Height: 2.30cm , Length: 23.50cm Weight: 1.720kg ISBN: 9780792345848ISBN 10: 0792345843 Pages: 424 Publication Date: 31 May 1997 Audience: College/higher education , Professional and scholarly , Undergraduate , Postgraduate, Research & Scholarly Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsEditorial.- Locally Weighted Learning.- Locally Weighted Learning for Control.- Voting over Multiple Condensed Nearest Neighbors.- Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching.- Discretisation in Lazy Learning Algorithms.- Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms.- The Racing Algorithm: Model Selection for Lazy Learners.- Context-Sensitive Feature Selection for Lazy Learners.- Computing Optimal Attribute Weight Settings for Nearest Neighbor Algorithms.- A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms.- Lazy Acquisition of Place Knowledge.- A Teaching Strategy for Memory-Based Control.- Lazy Incremental Learning of Control Knowledge for Efficiently Obtaining Quality Plans.- IGTree: Using Trees for Compression and Classification in Lazy Learning Algorithms.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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