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- W1520605446 abstract "An important issue in nearest-neighbor classifiers is how to reduce the size of large sets of examples. Whereas many researchers recommend to replace the original set with a carefully selected subset, we investigate a mechanism that creates three or more such subsets. The idea is to make sure that each of them, when used as a 1-NN subclassifier, tends to err in a different part of the instance space. In this case, failures of individuals can be corrected by voting. The costs of our example-selection procedure are linear in the size of the original training set and, as our experiments demonstrate, dramatic data reduction can be achieved without a major drop in classification accuracy." @default.
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- W1520605446 date "2001-12-28" @default.
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- W1520605446 title "A reduction technique for nearest-neighbor classification: Small groups of examples" @default.
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