Complete Cross-Validation for Nearest Neighbor Classifiers

@inproceedings{Mullin2000CompleteCF,
  title={Complete Cross-Validation for Nearest Neighbor Classifiers},
  author={Matthew D. Mullin and Rahul Sukthankar},
  booktitle={ICML},
  year={2000}
}
Cross-validation is an established technique for estimating the accuracy of a classifier and is normally performed either using a number of random test/train partitions of the data, or using kfold cross-validation. We present a technique for calculating the complete cross-validation for nearest-neighbor classifiers: i.e., averaging over all desired test/train partitions of data. This technique is applied to several common classifier variants such as K-nearest-neighbor, stratified data… CONTINUE READING
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