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在具有模式的完整统计知识条件下,按照贝叶斯决策理论进行设计的一种最优分类器。分类器是对每一个输入模式赋予一个类别名称的软件或硬件装置,而贝叶斯分类器是各种分类器中分类错误概率最小或者在预先给定代价的情况下平均风险最小的分类器。-In a model under the condition of complete statistical knowledge, in accordance with the Bayesian decision theory to design an optimal c
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用于分类规则挖掘的贝叶斯信念构造算法,用于分类规则挖掘的贝叶斯信念构造算法-For Classification Rule Mining Algorithm for Constructing Bayesian Belief for Classification Rule Mining Algorithm for Constructing Bayesian Belief
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贝叶斯决策理论:根据先验概率、类分布密度函数以及后验概率这些量来实现分类决策的方法.最小错误率的贝叶斯决策:根据一个事物后验概率最大作为分类依据的决策
-Bayesian decision theory: According to the a priori probability, the class distribution as well as the posterior probability density function of these values in order to a
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评测数据在去掉停用词的
分类过程开放测试中,引入Good-Turing算法的分类性能比Laplace原则提高了3·05 ,比Lidstone方法提高
1·00 .而在交叉熵选择特征词的算法中,增加Good-Turing的贝叶斯分类方法可比最大熵分类性能高95 .通过这种数据平滑的算法,有助于克服因数据稀疏而引发的特征词缺失问题
-Evaluation data in the open test of the classification process to remove stop
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This file details bayesian classification theorem in artificial intelligence
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An Efficient Bayesian Algorithm
for Joint Target Tracking and Classification,IEEE的论文, 有IEEE帐号的才能下载, 很珍贵-An Efficient Bayesian Algorithm
for Joint Target Tracking and Classification
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机器学习领域经典分类算法综述,包括Decision Tree(ID3、C4.5(C5.0)、CART、PUBLIC、SLIQ和SPRINT算法),三种典型贝叶斯分类器(朴素贝叶斯算法、TAN算法、贝叶斯网络分类器),k-近邻 、 基于数据库技术的分类算法( MIND算法、GAC-RDB算法),基于关联规则(CBA:Classification Based on Association Rule)的分类(Apriori算法),支持向量机分类,基于软计算的分类方法(粗糙集(rough set)、遗传
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相关向量机用于分类,可用于稀疏贝叶斯学习的研究,文章内含有RVM代码-Relevance vector machine for classification, can be used to study the sparse Bayesian learning, the article contains RVM code
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一种基于贝叶斯框架的线性分类。使用神经生理学信息和实验信息构建协方差矩阵。-A linear classification based on Bayesian framework. Covariance matrix is constructed using information and experimental neurophysiology information.
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Lung Cancer Detection and Classification Using ANN & Multinomial Bayesian Classifier
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