A Concrete Statistical Realization of Kleinberg’s Stochastic Discrimination for Pattern Recognition. Part I. Two-class Classification By

A Concrete Statistical Realization of Kleinberg's Stochastic Discrimination for Pattern Recognition, Part I. Two-Class Classification

A Statistical Look at Stochastic Discrimination

Stochastic discrimination (SD) has been shown to be a useful pattern recognition tool in the literature. A large number of experiments conducted indicate that SD has a low error rate. This paper presents a statistical look at SD. We show that for two-class problems SD simply transforms the multidimensional feature vectors to points coming from two univariate normal distributions. These two univ...

A Simple Implementation of the Stochastic Discrimination for Pattern Recognition

The method of stochastic discrimination (SD) introduced by Kleinberg ([6,7])is a new method in pattern recognition. It works by producing weak classifiers and then combining them via the Central Limit Theorem to form a strong classifier. SD is overtraining-resistant, has a high convergence rate, and can work quite well in practice. However, some strict assumptions involved in SD and the difficu...

دو روش تبدیل ویژگی مبتنی بر الگوریتم های ژنتیک برای کاهش خطای دسته بندی ماشین بردار پشتیبان

Discriminative methods are used for increasing pattern recognition and classification accuracy. These methods can be used as discriminant transformations applied to features or they can be used as discriminative learning algorithms for the classifiers. Usually, discriminative transformations criteria are different from the criteria of  discriminant classifiers training or  their error. In this ...

A Numerical Example on the Principles of Stochastic Discrimination

Studies on ensemble methods for classification suffer from the difficulty of modeling the complementary strengths of the components. Kleinberg’s theory of stochastic discrimination (SD) addresses this rigorously via mathematical notions of enrichment, uniformity, and projectability of a model ensemble. We explain these concepts via a very simple numerical example that captures the basic princip...