k-Nearest-Neighbor Classifiers These classifiers are memory-based, and require no model to be fit. Given a query point x0, we find the k training points x(r),r = 1,...,k closest in distance to x0, and then classify using majority vote among the k neighbors.

$k$-Nearest-Neighbor Classifiers These classifiers are memory-based, and require no model to be fit. Given a query point $x_0$, we find the $k$ training points $x(r), r = 1,...,k$ closest in distance to $x_0$, and then classify using majority vote among the $k$ neighbors.

SOURCE: The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie Robert Tibshirani Jerome Friedman. Second Edition February 2009 Springer (book: http://www-stat.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf)

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