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• W1599819030 abstract "We apply recent results on the minimax risk in density estimation to the related problem of pattern classification. The notion of loss we seek to minimize is an information theoretic measure of how well we can predict the classification of future examples, given the classification of previously seen examples. We give an asymptotic characterization of the minimax risk in terms of the metric entropy properties of the class of distributions that might be generating the examples. We then use these results to characterize the minimax risk in the special case of noisy two-valued classification problems in terms of the Assouad density and the Vapnik-Chervonenkis dimension." @default.
• W1599819030 created "2016-06-24" @default.
• W1599819030 creator A5006626558 @default.
• W1599819030 creator A5090849321 @default.
• W1599819030 date "1997-01-01" @default.
• W1599819030 modified "2023-09-24" @default.
• W1599819030 title "Metric entropy and minimax risk in classification" @default.
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• W1599819030 doi "https://doi.org/10.1007/3-540-63246-8_13" @default.
• W1599819030 hasPublicationYear "1997" @default.
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