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。
In this paper, the observed data in the Jiaozhou Bay are used to explore the application of Bayes classification method to the research field.
结合胶州湾实际调查数据，探讨了贝叶斯分类方法在该领域的应用。
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。
In the paper it puts forward and implements a hibernal tree automatic classification system based on Bayes.
提出并实现了一个基于贝叶斯的冬态树木自动分类的系统。
The classical Bayes method is used in the classification of pre-grasp of multiple fingers based on three patterns which are grasping, holding and pinching.
运用经典的贝叶斯决策方法将预抓取物体进行基本的三类抓、握、捏抓取模式分类。
Bayesian methods are those that are explicitly apply Bayes' Theorem for problems such as classification and regression.
贝叶斯方法是那些明确地在分类和回归问题中应用贝叶斯定理的算法。
Comparing with Bayes method-the classical algorithm, we conclude that the neural network is better than Bayes method. This paper gives all the procedures of SAR image classification.
并且与图像分类中统计方法的经典算法贝叶斯分类方法做了比较，结果发现，神经网络分类方法的分类效果要优于贝叶斯方法。
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。
There are many classification methods to forecast such as decision tree algorithm (C4.5), Bayes algorithm, BP algorithm and SVM.
现有的分类预测的方法有许多种，常见的有决策树算法(C4.5)、贝叶斯分类算法、BP算法与支持向量机等。
The experimental results show that Bayes attributes-depending classification has a better performance of classification.
实验结果表明，属性依赖贝叶斯方法有较好的分类性能。
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。
The classification accuracy of Bayes is up to 82.89%, and the classification accuracy of SVM is up to 83.34%.
贝叶斯的分类准确性最高可达82.89%，支持向量机的分类准确性最高可达83.34%。
The classification accuracy of Bayes is up to 82.89%, and the classification accuracy of SVM is up to 83.34%.
贝叶斯的分类准确性最高可达82.89%，支持向量机的分类准确性最高可达83.34%。
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