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: 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: 这是用身高体重数据进行性别分类的实验。
用最小错误率贝叶斯分类器决策时，首先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，然后再逐一与已知性别的数据比较，就可以得到错误率的统计。然后改变先验概率，重复上面的过程，观察数据结果的变化。
用最小风险贝叶斯分类器决策时，首先求出用最小错误率贝叶斯分类器得到的条件概率；然后根据人为给定的决策表，根据公式算出条件风险；然后逐一比较条件风险，找出使条件风险最小的决策（也就是分类）。最后用分类得到的结果逐一比较已经知道的原始数据，统计处错误率。
-This is the height and weight data for gender classification experiment.
With the minimum error rate Bayesian classifier decisions , first by comparing the probability of the size and weight to height to determine a person represented by two-dimensional vector is male or female , and then one by one with known gender data comparison, the statistical error rate can be . Then change the prior probability , repeat the above process , the results of the changes observed data .
Bayesian classifier with the minimum risk decision-making , first find the minimum error rate using Bayesian classifier to get the conditional probability then artificially given decision table , according to the formula to calculate conditional risk and then one by one more conditional risk , to find ambassador to the conditions of minimum risk decision making (ie classification) . Finally, the results obtained with the classification by-side comparison of the raw data have been aware of SD error rate .
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Size: 90112 |
Author:崔杉 |
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Description: 基于最小错误概率和最小风险的贝叶斯分类器-Based on the minimum probability of error and minimum- risk Bayesian classifier
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Size: 1024 |
Author:醉月 |
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Description: 模式识别实验：贝叶斯分类器的设计，以及实现了最小风险贝叶斯决策理解二类分类器的设计，matlab编程，有详细的注释-
模式识别实验：贝叶斯分类器的设计，以及实现了最小风险贝叶斯决策理解二类分类器的设计，matlab编程，有详细的注释
Pattern recognition classifier design experiment: Bias, as well as the realization of the design, the minimum risk Bias decision to understand two class classifier of MATLAB programming, with detailed notes
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Size: 2048 |
Author:亢菲菲 |
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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: 其中贝叶斯分类包括最小错误率和最小风险两种
新手看看吧。也不错的-Bayesian classifier which comprises a minimum error rate and minimum risk two kinds novice to see it. Not bad Platform: |
Size: 5120 |
Author:asdzxc |
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Description: 实现贝叶斯分类器，按最大概率和最小风险的分类决策-Implement Bayesian classifier, according to the maximum and minimum risk probability of classification decisions Platform: |
Size: 1024 |
Author:zzh |
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