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

armistice

Editorial,er algorithms during training, they typically have greater storage requirements and often have higher computational costs when answering requests. For the first time, this distinction, and its implications, are the focus of a (quintuple) special issue; . has brought together 14 articles that review

Encoding

Voting over Multiple Condensed Nearest Neighbors,te size, we use bootstrapping to generate smaller training sets over which we train the voters. When the training set is large, we partition it into smaller, mutually exclusive subsets and then train the voters. Simulation results on six datasets are reported with good results. We give a review of m

拥护者

Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching,-learning algorithms to have good classification accuracy in conditions having a time-varying function mapping and input sampling distributions, while still maintaining their asymptotic classification accuracy in static tasks. PECS works by selecting and re-activating previously stored instances bas

Painstaking

The Racing Algorithm: Model Selection for Lazy Learners,sing leave-one-out cross validation is efficient. We use racing to select among various lazy learning algorithms and to find relevant features in applications ranging from robot juggling to lesion detection . MRI scans.

Noisome

orthodox

folliculitis

Lazy Acquisition of Place Knowledge,Previous researchers have studied evidence grids and place learning, but they have not combined these two powerful concepts, nor have they used systematic experimentation to evaluate their methods’ abilities.

CAND

minimal

强制性

IGTree: Using Trees for Compression and Classification in Lazy Learning Algorithms,dicate that IGTree is a useful algorithm for problems characterized by the availability of a large number of training instances described by symbolic features with sufficiently differing information gain values.