Tubular Neighbors for Regression and Classiication
نویسنده
چکیده
The simple k nearest neighbor method is often very competitive, especially in classiication methods. When the number of predictors is large, the nearest neighbors are likely to be quite distant from the target point. Furthermore they tend to all be on one side of the target point. These are consequences of high dimensional geometry. This paper introduces a modiication of nearest neighbors that explicitly takes into account the extrapolation required in high dimensions.
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تاریخ انتشار 1999