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.

Editorial.- 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.