Flexible Metric Nearest Neighbor Classi ̄ cation

@inproceedings{Friedman1994FlexibleMN,
  title={Flexible Metric Nearest Neighbor Classi ̄ cation},
  author={Jerome H. Friedman},
  year={1994}
}
The K-nearest-neighbor decision rule assigns an object of unknown class to the plurality class among the K labeled \training" objects that are closest to it. Closeness is usually de ̄ned in terms of a metric distance on the Euclidean space with the input measurement variables as axes. The metric chosen to de ̄ne this distance can strongly e®ect performance. An optimal choice depends on the problem at hand as characterized by the respective class distributions on the input measurement space, and… CONTINUE READING
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