Titlebook: Lazy Learning; David W. Aha Book 1997 Springer Science+Business Media Dordrecht 1997 algorithms.case-based reasoning.classification.cognit

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Locally Weighted Learning for Control,f complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting

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Voting over Multiple Condensed Nearest Neighbors,condensed nearest neighbor classifier incrementally stores a subset of the sample, thus decreasing storage and computation requirements. We propose to train multiple such subsets and take a vote over them, thus combining predictions from a set of concept descriptions. We investigate two voting schem

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Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching,at occur in concept shift. Extensions of these algorithms, such as Time-Windowed forgetting (TWF), can permit learning of time-varying mappings by deleting older exemplars, but have decreased classification accuracy when the input-space sampling distribution of the learning set is time-varying. Addi

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