On The Finite-Sample Performance Of The Nearest-Neighbor Classifier

The finite sample performance of a nearest neighbor classifier is analyzed for a two-class pattern recognition problem. An exact integral expression is derived for the m-sample risk R(m) given that a reference m-sample of labeled points is available to the classifier. The statistical setup assumes that the pattern classes arise in nature with fixed a priori probabilities and that points representing the classes are drawn from Euclidean n-space according to fixed class-conditional probability distributions. The sample is assumed to consist of m independently generated class-labeled points. For a family of smooth class-conditional distributions characterized by asymptotic expansions in general form, it is shown that the m-sample risk R(m) has a complete asymptotic series expansion


Published in:
Ieee Transactions On Information Theory, 40, 820-837
Year:
1994
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 Record created 2010-11-25, last modified 2018-03-17

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