K-means-clustering-experiments

采用爬山法求解优化问题，一共4个文件，函数和脚本分开放(Climbing method used optimization problem, a total of four documents, functions and sub-script open)

说明：  利用bp算法对鸢尾花数据进行分类的matlab实现程序(Bp algorithm using iris data classification procedures to achieve matlab)

采用Matlab工具箱函数建立神经网络，对一些基本的神经网络参数进行了说明，深入了解参考Matlab帮助文档。 (Using Matlab toolbox functions to establish neural networks, some of the basic neural network parameters are described to understand the reference to Matlab help documentation.)

说明：  基于MATLAB的语音信号处理之滤波器设计(MATLAB-based speech signal processing of the filter design)

相位编码脉冲压缩雷达的多普勒补偿算法(详细)，matlab开发仿真 (Phase-coded pulse compression radar Doppler compensation algorithm (detail), matlab development simulation)

matlab 用于sis模型的研究 一篇论文 希望有用(matlab sis)

自己写的Floyd,没有理解Folyd的同学可以拿去参考一下啦!!!!(Wrote it myself Floyd, do not understand Folyd students can refer to you instead!!!!)

ERRandomGraph matlab代码构造ER随即网络图(matlab for ERRandomGraph)

霍夫曼coding程序，用此程序可以对任意图像进行霍夫曼编码。(Huffman coding procedures, this procedure can be used for any image Huffman coding.)

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 )