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October 2003 A concrete statistical realization of Kleinberg's stochastic discrimination for pattern recognition. Part I. Two-class classification
Dechang Chen, Peng Huang, Xiuzhen Cheng
Ann. Statist. 31(5): 1393-1413 (October 2003). DOI: 10.1214/aos/1065705112

Abstract

The method of stochastic discrimination (SD) introduced by Kleinberg is a new method in statistical pattern recognition. It works by producing many weak classifiers and then combining them to form a strong classifier. However, the strict mathematical assumptions in Kleinberg [The Annals of Statistics 24 (1996) 2319-2349] are rarely met in practice. This paper provides an applicable way to realize the SD algorithm. We recast SD in a probability-space framework and present a concrete statistical realization of SD for two-class pattern recognition. We weaken Kleinberg's theoretically strict assumptions of uniformity and indiscernibility by introducing near uniformity and weak indiscernibility. Such weaker notions are easily encountered in practical applications. We present a systematic resampling method to produce weak classifiers and then establish corresponding classification rules of SD. We analyze the performance of SD theoretically and explain why SD is overtraining-resistant and why SD has a high convergence rate. Testing results on real and simulated data sets are also given.

Citation

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Dechang Chen. Peng Huang. Xiuzhen Cheng. "A concrete statistical realization of Kleinberg's stochastic discrimination for pattern recognition. Part I. Two-class classification." Ann. Statist. 31 (5) 1393 - 1413, October 2003. https://doi.org/10.1214/aos/1065705112

Information

Published: October 2003
First available in Project Euclid: 9 October 2003

zbMATH: 1100.68609
MathSciNet: MR2012819
Digital Object Identifier: 10.1214/aos/1065705112

Subjects:
Primary: 68T10
Secondary: 68T05

Rights: Copyright © 2003 Institute of Mathematical Statistics

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Vol.31 • No. 5 • October 2003
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