Description: 通过设计线性分类器；最小风险贝叶斯分类器；监督学习法分层聚类分析；K－L变换提取有效特征，设计支持向量机对给定样本进行有效分类并分析结果。-By designing a linear classifier minimum risk Bayes classifier supervised learning method hierarchical cluster analysis K-L transform to extract efficient features, designed to support vector machines for effective classification of a given sample and analyze the results. Platform: |
Size: 2599936 |
Author:于冰 |
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Description: 利用贝叶斯实现的分类器小程序，显示出按照最小风险和最小误差分类的结果-Bayesian classifier implemented using a small program to show in accordance with the minimum risk and minimum error classification results Platform: |
Size: 2048 |
Author:haiyang |
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Description: 使用Matlab实现，包括一维特征最小错误率bayes分类器；二维特征最小错误率bayes分类器；二维特征最小风险bayes分类器以及使用的数据集合。-Using the Matlab implementation, including the minimum error rate of one-dimensional characteristics of bayes classifier two-dimensional characteristics of the minimum error rate bayes classifier two-dimensional characteristics of the minimum risk bayes classifier and the use of data collection. Platform: |
Size: 4096 |
Author:郭鹏宇 |
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Description: 基于bayes准则和fisher准则的线性分类器。bayes是对细胞的判别分类 分为最小错误率和最小风险两种；fisher是对两类进行判别-Bayes guidelines and criteria based on fisher linear classifier. bayes classification is divided into cells, determine the minimum error rate and minimal risk of two kinds fisher is to discriminate two types of Platform: |
Size: 4096 |
Author:小祉 |
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Description: 这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。
全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。
调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。
调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，通过比较概率大小判断一个体重身高二维向量代表的人是男是女，放入决策数组中。
主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。
-This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array .
All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions .
Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function .
Bayesian classifier Platform: |
Size: 4096 |
Author:崔杉 |
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Description: 本程序主要实现了基于贝叶斯分类的手写数字识别。
1、主程序为recognition.m，运行程序只需打开recognition.m文件运行即可
2、byes算法程序分别在bayes_erzhi.m文件、bayes_leasterror.m文件和bayesleastrisk.m文件中，分别对应着基于二值数据的bayes法的算法和基于最小错误的bayes法的算法和基于最小风险的贝叶斯分类器。
-The program achieved a handwritten numeral recognition based on Bayesian classification. 1, the main program is recognition.m, simply open recognition.m file to run the program can be run 2, byes algorithm respectively bayes_erzhi.m file, bayes_leasterror.m files and bayesleastrisk.m files, corresponding to a value based on two algorithms and data bayes method based on the minimum error of law bayes algorithms and minimum risk-based Bayesian classifier. Platform: |
Size: 332800 |
Author:严娟 |
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Description: 概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出，是径向基网络的一个分支，属于前馈网络的一种。它具有如下优点：学习过程简单、训练速度快；分类更准确，容错性好等。从本质上说，它属于一种有监督的网络分类器，基于贝叶斯最小风险准则。(Probabilistic neural network was first proposed by Dr. D.F.Speeht in 1989. It is a branch of radial basis networks and belongs to a feedforward network. It has the following advantages: the learning process is simple, the training speed is fast, the classification is more accurate, and the fault tolerance is good. Essentially, it belongs to a supervised network classifier based on the Bayes minimum risk criterion.) Platform: |
Size: 5120 |
Author:gahuan
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