构造了保持隐私的朴素贝叶斯分类器。
We construct the privacy preserving Naive Bayesian Classifier.
该文利用特征加权技术来增强朴素贝叶斯分类器。
In this paper, we investigate enhancement of naive Bayes classifier using feature weighting technique.
利用稀疏贝叶斯分类器对检测到的汽车进行车型分类。
Sparse Bayesian classification is applied to recognize and classify vehicle models.
论文研究了三种朴素贝叶斯分类器信用评估模型的精度。
This paper investigates the credit scoring accuracy of three naive Bayesian classifier models.
实验表明提出的选择性贝叶斯分类器适于变压器故障诊断。
Experimental results show that Bayes classifier is suitable for the transformer fault diagnosis.
本文设计了一个有效的基于贝叶斯分类器的中文期刊自动分类系统。
This paper presents an efficient automatic categorization system for Chinese journals based on Bayes classifier.
本文基于粗糙集理论探索特征加权技术对朴素贝叶斯分类器的改进。
In this paper, we investigate enhancement to naive Bayes classifier using feature weighting technique based on rough set theory.
摘要：文中研究贝叶斯分类器家族中的一种扩展朴素贝叶斯分类器。
Absrtact: An augmented naive Bayes classifier of Bayes classifier family is studied in this paper.
就算该假设不成立，朴素贝叶斯分类器在实践中仍然有着不俗的表现。
And even if the NB assumption doesn't hold, a NB classifier still often does a great job in practice.
对文档模型的分类，采用了贝叶斯分类器，并动态调整反馈器的参数。
The classification of the document model, using Bayesian classifier, can dynamically adjust the parameters of feedback devices.
与两种基于免疫原理的文本分类方法和传统的贝叶斯分类器进行了比较研究。
It is compared with two kinds of classifier, which is based on the principle of immunity, and traditional Bayesian classification.
此种扩展朴素贝叶斯分类器满足两个条件：一是类结点是所有属性的父结点；
This classifier is defined by the following two conditions-one is that each attribute has the class attribute as parent;
为了测试评估贝叶斯分类器的性能，用不同数据集进行对比实验是必不可少的。
In order to test the assessment of the performance of Bayesian classifier, and compare different experimental data sets is essential.
讨论了离散贝叶斯分类算法之后，推导了离散贝叶斯分类器的分类误差估算公式。
The algorithm of discrete Bayes classifier is proposed. Then, formulas for estimating classifying error of Bayes classifier are deduced.
根据累积量和主运动方向提取出3维特征矢量，采用贝叶斯分类器进行烟雾的检测。
A 3d feature is extracted from the accumulation and main motion orientation, and a Bayesian classifier is used for smoke detection.
本文采用贝叶斯分类器，提取问句主干以及包含疑问词的分支作为特征进行问题分类。
Then we employ Bayesian classifier to classify these questions. Answer extraction is the most crucial part for question answering system.
TAN分类器按照一定的结构限制，通过添加扩展弧的方式扩展朴素贝叶斯分类器的结构。
TAN classifier extends the structure of Naive Bayes classifier by adding augmenting arcs that obey certain structural restrictions.
实验结果表明，在大多数实验数据上，PEBNC能够明显提高贝叶斯分类器的分类准确率。
The experimental results indicate that the PEBNC classifier can improve the classification performance in most cases.
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 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 based on probability theory, but its attribute independence assumption is often violated in the real world.
在文本分类器的设计中，用传统信息检索的空间向量模型改进了朴素贝叶斯分类器，提高了它的分类精度。
In designing web Classifier, this thesis makes use of Vector Space Model to represent the web text, which improves the performance of Bayes Classifier.
其次，通过对图书馆的样本数据进行训练建立的分类库，本文使用贝叶斯分类器实现中文期刊的自动分类。
Then, giving the sample words from library, a categorization database is created and used for automatic categorization of Chinese journals by Bayesian learning.
的翻译是：本文件采用一条中间道路：它结合了协会与abnamro分类规则，构建一个贝叶斯分类器。
This paper adopts a middle way: It combines association rules with the ABN classification to construct a Bayesian classifier.
文中提出了一种新的结构学习TANC - CBIC算法。并在贝叶斯分类器实验平台MBNC上编程实现。
This paper suggests a new structure-learning algorithm called TANC-CBIC, makes experiment in MBNC experiment platform with programming TANC-CBIC algorithm.
摘要：朴素贝叶斯分类器是一种简单而高效的分类器，但它的条件独立性假设使其无法表示属性问的依赖关系。
Absrtact: Naive Bayesian classifier is a simple and effective classifier, but its conditional independence assumption makes it unable to express the dependence among features.
树扩展型朴素贝叶斯（TAN）分类器放松了朴素贝叶斯的属性独立性假设，是对朴素贝叶斯分类器的有效改进。
TAN(tree augmented Nave Bayes) takes the Nave Bayes classifier and adds edges to it, it is efficient extend of Nave Bayes.
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. Classifying based on Bayes Technology has got more and more attentions in the field of data mining.
倘若条件独立性假设确实满足，朴素贝叶斯分类器将会比判别模型，譬如逻辑回归收敛得更快，因此你只需要更少的训练数据。
If the NB conditional independence assumption actually holds, a Naive Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data.
为了解决该方法存在的训练数据集问题，本文改进了现有的贝叶斯分类算法，提出了利用未标记数据提高贝叶斯分类器性能的方法。
In order to solve the problem existing in training data sets, present Bayes algorithm is im - proved and an algorithm using unlabeled data to improve the capability of the classifier is proposed.
研究了人脸检测的贝叶斯特征判别法，该方法包括三个部分：原始图像的特征判别分析、人脸区和其它区的统计建模以及贝叶斯分类器。
The main idea of which consists of three parts: the discriminating feature analysis of the images, the statistical modeling of face and non-face classes, and the Bayes classifier for face detection.
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