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MC
说明： 关于蒙特·卡罗方法的一个简单算例；用来计算椭圆面积(Monte Carlo method)
- 2011-03-24 20:58:26下载
- 积分：1
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source
说明： 《matlab7.0 从入门到精通》源码(source of the book <<matlab7.0>> ,has many examples)
- 2009-09-01 20:10:10下载
- 积分：1
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BURG
BURG方法谱估计 采用BURG迭代方法计算系数，在采用AR模型的谱估计进行谱峰搜索(BURG spectrum estimation methods BURG iterative method using coefficients in the AR model spectrum estimation using the peak search)
- 2011-06-28 22:10:17下载
- 积分：1
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doubleslit
Double silt experiment matlab calculation of m-file
- 2014-01-09 23:28:37下载
- 积分：1
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MyRearch
包含matlab及C++对图像的基本操作，如：边缘提取，配准等等(Contains matlab and C++ the basic operation of the image, such as: edge detection, registration, etc.)
- 2012-04-16 19:39:18下载
- 积分：1
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SOA
用MATLAB编程实现半导体光放大器（SOA）的模拟(MATLAB programming realize semiconductor optical amplifiers (SOA) simulation
)
- 2012-05-13 09:59:07下载
- 积分：1
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xianxingguihua
用单纯形法求解线性规划
用完整单纯形法求解线性规划
用修正单纯形法求解线性规划
(Complete simplex method for solving linear programming using the simplex method for solving linear programming using the revised simplex method for solving linear programming)
- 2013-03-10 18:28:29下载
- 积分：1
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Minimum-Bayes-classifier-error-rate
这是模式识别中最小错误率Bayes分类器设计方案。
自行完善了在不同先验概率的条件下，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。
调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，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。
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”，调用最小错误率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，就可以得到不同先验概率条件下错误率的统计。
(This is the minimum error rate pattern recognition Bayes classifier design.
Self- improvement prior probability in different 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 third step is to 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 .
Call the minimum error rate decision Functions Bayesian)
- 2012-02-02 20:33:06下载
- 积分：1
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GL2JavaView
number of errors reported in a file. int errors .
- 2014-02-24 09:42:26下载
- 积分：1
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rbmcda_1_0
rao-blackwellised data association partical filters
- 2010-07-21 23:05:46下载
- 积分：1
