A Recursive Partitioning Decision Rule for Nonparametric Classification
作者： Friedman
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摘要: A new criterion for deriving a recursive partitioning decision rule nonparametric classification is presented. The both conceptually and computationally simple, can be shown to have strong statistical merit. resulting asymptotically Bayes' risk efficient. notion of adaptively generated features introduced methods are presented dealing with missing in training test vectors.
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sci-hub.se 参考文章(7)
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