chap14

Visual c++Matlab图像处理与实用案例精选:图像镜像(Visual c Matlab Image Processing and practical case Featured : Mirror Image)

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 )

说明：  matlab 开发用到的各种实例 非常实用 分享(matlab developed a variety of examples used)

可以进行数据归一化处理 已进行模糊聚类 打基础 很好的一个程序(Data can be normalized fuzzy clustering has been laying the foundation for a good program)

apf with different controllers

Maximum Likelihood Estimation of Batch Perceptron Convergence

用matlab来生成全息图的一个样例程序。简单实用(Using matlab to generate a sample program hologram. Simple and practical)

microprocessor basic theory

灰色预测的问题，基本上稍微改动即可。希望对大家有所帮助。(Grey in question is basically a little modification to)

分枝定界法程序，解混合整数线性规划《详解MATLAB在最优化计算中的应用》中的分枝定界法程序，稍作了修改(Branch and bound method procedures, the solution mixed integer linear programming, " Detailed MATLAB optimization calculation in the application" in the branch and bound procedure, slightly modified)