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About this paper
Hyun-Chul Kim
The authors introduce a method for selecting k in mutual- and symmetric-neighbor classifiers using Gaussian-process regression. It encodes class labels as regression targets and selects k by maximizing marginal likelihood. On New Thyroid, symmetric voting with evidence-selected k had lower mean error than with cross-validation-selected k in 10-fold testing; results across three datasets do not establish uniform gains.
Summary by alphaXiv.
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