The experiment of Naive Bayes classification indicates that this method can effectively improve classification precision of Chinese texts.
基于朴素贝叶斯分类方法的实验表明，提出的方法能够有效提高中文文本的分类准确率。
This paper focuses on privacy preserving classification, and presents a privacy preserving Naive Bayes classification approach based on data randomization and feature reconstruction.
围绕着分类挖掘中的隐私保护问题展开研究，给出了一种基于数据处理和特征重构的朴素贝叶斯分类中的隐私保护方法。
Naive Bayes classification is a kind of simple and effective classification model. However, the performance of this model may be poor due to the assumption on the condition independence.
朴素贝叶斯分类是一种简单而高效的分类模型，然而条件独立性假设在现实中很少出现，致使其性能有所下降。
Naive Bayes classifier is a simple and effective classification method based on probability theory, but its attribute independence assumption is often violated in the real world.
朴素贝叶斯分类器是一种简单而有效的概率分类方法，然而其属性独立性假设在现实世界中多数不能成立。
Naive Bayes classifier is a simple and effective classification method. Classifying based on Bayes Technology has got more and more attentions in the field of data mining.
朴素贝叶斯分类器是一种简单而高效的分类器，基于朴素贝叶斯技术的分类是当前数据挖掘领域的一个研究热点。
Naive Bayes classifier is a simple and effective classification method, but its attribute independence assumption makes it unable to express the dependence among attributes in the real world.
朴素贝叶斯分类器是一种简单而高效的分类器，但是其属性独立性假设限制了对实际数据的应用。
Naive Bayes algorithm is a simple and effective classification algorithm. However, its classification performance is affected by its conditional attribute independence assumption.
朴素贝叶斯算法是一种简单而高效的分类算法，但是它的条件独立性假设影响了其分类性能。
Naive Bayes algorithm is a simple and effective classification algorithm. However, its classification performance is affected by its conditional attribute independence assumption.
朴素贝叶斯算法是一种简单而高效的分类算法，但是它的条件独立性假设影响了其分类性能。
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