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首页> 外文期刊>The Annals of Statistics: An Official Journal of the Institute of Mathematical Statistics >A concrete statistical realization of Kleinberg's stochastic discrimination for pattern recognition. part I. Two-class classification
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A concrete statistical realization of Kleinberg's stochastic discrimination for pattern recognition. part I. Two-class classification

机译：Kleinberg随机识别模式识别的具体统计实现。第一部分。两类分类

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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. [References: 23]
机译：Kleinberg提出的随机判别（SD）方法是一种统计模式识别的新方法。它的工作原理是产生许多弱分类器，然后将它们组合以形成一个强分类器。但是，在实践中很少满足Kleinberg [统计学年鉴24（1996）2319-2349]中严格的数学假设。本文提供了一种实现SD算法的适用方法。我们在概率空间框架中重塑了SD，并提出了用于两类模式识别的SD的具体统计实现。通过引入接近均匀性和弱不可分辨性，我们削弱了Kleinberg对均匀性和不可分辨性的理论严格假设。在实际应用中很容易遇到这种较弱的概念。我们提出了一种系统的重采样方法来产生弱分类器，然后建立相应的SD分类规则。我们从理论上分析了SD的性能，并解释了SD为什么耐过度训练以及SD为什么具有较高的收敛速度。还给出了真实和模拟数据集的测试结果。 [参考：23]

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