一种新的Naïve贝叶斯分类器方法

B. Singh, Anupam Agarwal
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引用次数: 0

摘要

贝叶斯理论是设计分类器的一个很好的基础。有几个分类器并不严格遵守贝叶斯原理,但从理论本身出发,被称为朴素贝叶斯方法。本工作提出了一种朴素贝叶斯分类方法,该方法将分类过程限制在关键比较的基本操作上。此外,我们还可以用普通代数的方法将合成器中的概率-频率概念和合理期望概念联系起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel approach towards Naïve Bayesian classifier
Bayesian Theory has been an elegant basis for the design of classifiers. There are several classifiers which do not adhere with the Bayesian principle strictly but motivated from the very theory, termed as naive Bayesian methods. The present work proposes a naive Bayesian Classification approach which limits the classification process to the basic operation of the key comparison. Moreover we are able to link the two notions of the probability — frequency in ensemble and idea of reasonable expectation by the means of ordinary algebra.
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