贝叶斯分类

基于贝叶斯分类的水平集MR图像分割方法

A Level Set Method Based on Bayesian Classification for Image Segmentation in MRI

面向CRM的贝叶斯分类算法及并行化研究

The Parallel of Bayesian Classification Algorithms for CRM

基于RoughSet的加权朴素贝叶斯分类算法

Weighted Naive Bayes Classification Algorithm Based on Rough Set

SVM回归与朴素贝叶斯分类相结合的变压器故障诊断

Transformer diagnosis by Naive Bayesian classifier combing with SVM regression

一种HTML文档的朴素贝叶斯分类算法

An improved naive bayesian categorization algorithm for HTML

基于一类SVM的贝叶斯分类算法

A Bayesian Classification Algorithm Based-on One-Class SVM

贝叶斯分类法在MCS移动路径预测中的应用

Application of Bayes Classification in Forecasting the Trajectories of MCS

介绍分析聚类分析中的k-means算法和朴素贝叶斯分类算法；

Introducing and analyzing k-means algorithms of clustering algorithms and Naive Bayesian Classifier algorithm .

基于Dirichlet分布的贝叶斯分类算法的手写数字字符识别

Handwritten digital recognition based on Dirichlet distribution and Bayesian classifier

该模型以实时抽样流量作为数据来源，采用关联分析法提取可信IP列表用于数据包的过滤，并利用贝叶斯分类算法对数据包的危险等级进行评估。

Based on real-time sample traffic , this model extracts trusted IP list by association analysis to filter , and evaluates packets danger degree by adopting bayes algorithm .

本文中用到的方法有SVM分类方法、贝叶斯分类方法、简单向量距离法和多组判别分析法。

In this paper it uses SVM text classification , multi-group distinguishing text classification , Naive Byes text classification and simple vector distance text classification .

一种采用LLE降维和贝叶斯分类的多类标学习算法

Multi-label learning by LLE dimension reduction and Bayesian classification

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。

There are many classification methods to forecast such as decision tree algorithm ( C4.5 )、 Bayes algorithm 、 BP algorithm and SVM .

借鉴K-means算法，用朴素贝叶斯分类算法来解决分类问题，既能发挥K-means算法的局部搜索能力，又能提高朴素贝叶斯分类的准确度，从而更好地解决分类问题。

The local searching ability of k-means algorithm is exerted , and the precise of Naive Bayesian Classifier improved . It can solve classification problem effectively .

实验表明，用贝叶斯分类方法比传统的Fisher分类算法能更好地对手写数字字符进行分类识别，且在众多领域中有较大的应用价值。

Experimental results show that the arithmetic is better than traditional Fisher classifying arithmetic to recognize handwritten numbers . It has been used successfully in many fields .

提出一种基于一类支持向量机（one-ClassSVM）的贝叶斯分类算法，该算法用一类SVM对类条件概率密度进行估计以构造贝叶斯分类器。

A Bayesian classification algorithm based on one-class SVM is presented . It constructs the Bayesian classifier using the classes ' conditional density estimated by one-class SVM .

为了降低朴素贝叶斯分类模型的独立性假设约束，提出一种混合式朴素贝叶斯分类模型（MBN：MixedNaiveBayes）。

In order to decrease the attribute independence assumption which is made by Naive Bayesian , a new Bayesian model MBN ( Mixed Naive Bayes ) is introduced .

本文在原有的贝叶斯分类的基础上进行了改进，提出了一种基于Dirichlet分布的贝叶斯分类模型，对手写数字字符进行识别的算法。

This paper introduces a Bayesian classifying model based on the Dirichlet prior distribution which is improved by existed Bayesian model to recognize handwritten numbers .

基于RoughSet的属性重要性理论，提出了基于RoughSet的加权朴素贝叶斯分类方法，并分别从代数观、信息观及综合代数观和信息观的角度给出了属性权值的求解方法。

Based on the attributes ' importance degree theory of rough set , a new weighted naive Bayes method is proposed . Methods for determining the weights of attributes in the algebra view , informational view and both of them are developed respectively .

模糊角分类神经网络模型根据用户信息所落入的k最近邻的样本泛化空间来进行分类，随着k值的增大，其分类效果趋近于贝叶斯分类算法。

FCC neural network model works according to the k-nearest neighbor samples ' generalization space which users ' information falls into . With the increasing of value k , the classification effect becomes close to that of Bayes classification algorithm .

将聚类算法引入到朴素贝叶斯分类研究中，提出一种基于聚类的朴素贝叶斯分类算法（CNBC）。

A Naive Bayesian classification based on clustering principle ( CNBC ) by introducing clustering algorithm into Naive Bayesian classification .

根据RoughSet的相关理论，提出了基于条件信息熵的自主式朴素贝叶斯分类方法，该方法结合了选择朴素贝叶斯和加权朴素贝叶斯的优点。

Based on the theory of rough set , a new Nave Bayes method named Conditional Information Entropy-based Algorithm for Self-learning Nave Bayes ( CIEBASLNB ) was proposed , which combined the merits of selective Nave Bayes ( SNB ) and Weighted Nave Bayes ( WNB ) .

AFCAS系统采用了边界链码和贝叶斯分类的图像识别技术和基于ADO的数据库技术。

The AFCAS System adopts the image recognition technology of Region Chain Code and Bayesian Classification and ADO-Based database technology .

本文通过对增量朴素贝叶斯分类算法的研究，将增量贝叶斯分类器应用于主题爬虫主题相关度的计算中。最后采用C++语言在Linux环境下对主题爬虫进行了实现。

By the research of incremental bayes classifier algorithm in this thesis , it would be used in the topic relevant calculation of the topic crawler . Finally , implement the topic crawler under the circumstance of Linux by C + + language .

数据集成过程中，利用基于权值的朴素贝叶斯分类方法对承载异构数据的XML文档进行集成，其目标是向态势感知传感器的上层，也就是更高层应用用户提供具有统一格式的数据。

In the process of data integration , the method of weight based na ? ve Bayes classifier integrates the XMLs contains heterogeneous data , the target of which is to provide uniform format data to higher class user which is higher class of situational awareness sensor .

同时，结合数据挖掘工具weka证明，在不断对贝叶斯分类进行动态调整之后可使分类结果达到最优。

Meanwhile , the combination of data mining tools that weka , constantly on the Bayesian classifier can be dynamically adjusted to achieve optimal classification results .

通过分析目前回转窑过程控制技术的发展趋势和特点，设计了一种基于ICA贝叶斯分类的回转窑喂煤预测模型，并用于现场控制。

Based on analyzing both the advantages and disadvantages of current control technology of rotary kiln , we designed a coal feed predictive module in rotary kiln based on bayesian classification , and applied it to field control .

目前，国内外学者们已经研究出了多种检测方法，主要包括贝叶斯分类，数据挖掘，专家系统，神经网络，人工免疫系统，Petri网，Markov链，自治Agent，支持向量机（SVM）等方法。

At present , domestic and foreign scholars have developed a variety of detection methods , mainly including Bayesian classification , data mining , expert systems , neural networks , artificial immune systems , Petri nets , Markov chains , autonomous Agent , support vector machines ( SVM ) .

文本分类用的是简单的贝叶斯分类方法，在特征提取的过程中，采用TF-IDF值作为特征选择的标准，并用三元组中心词来限定选取范围，达到自然降维的目的。

Text classification takes simple Bayes classification method . The feature extraction use TF-IDF as selection criteria , and by sentences ' three-tuple words to define selection range , which achieve the purpose of natural dimension .

本文引入统计学中的Kullback-Leibler距离来衡量属性的重要度，提出基于KL距离的加权朴素贝叶斯分类算法（AWNB-KL），提高了朴素贝叶斯算法的性能。（3）实际运用方面。

In this thesis , Kullback-Leibler distance is introduced to value the importance of each at-tribute . A new algorithm called the weighted Naive Bayesian based on Kullback-Leibler distance ( AWNB-KL ) is proposed , and the experiment shows its great performance . ( 3 ) Practical application .