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16QAM
Digital transmission using 16qam
- 2010-06-28 23:18:06下载
- 积分:1
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Acupuncture
This m file for acupuncture points are made in the human body
- 2011-12-29 14:00:51下载
- 积分:1
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梯度投影法matlab源码程序
说明: 关于梯度投影算法的一个简单的例子的matlab源码程序(Gradient projection method matlab source program)
- 2020-12-24 10:49:06下载
- 积分:1
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cover_tree.tar
A Cover Tree is a datastructure helpful in calculating the nearest neighbor of points given only a metric.
- 2010-09-22 10:17:19下载
- 积分:1
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lsm
说明: 递推最小二乘法,根据最小二乘法改变,可以辨识参数(LSM)
- 2010-04-15 22:23:16下载
- 积分:1
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ParticleEx3
particle filter example using matlab
- 2011-12-05 18:50:15下载
- 积分:1
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SGA
Simple Genetic Algorithm
- 2012-05-12 06:31:28下载
- 积分:1
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introduction-of-MATLA
matlab的学习教程,里面含有很全面的介绍教程是ppt格式的,很容易理解,有利于初学matlab的同学的学习(matlab learning tutorial, which contains a very comprehensive introduction tutorial ppt format, it is easy to understand, is conducive to learning for students to learn matlab)
- 2013-09-08 18:32:21下载
- 积分:1
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0.618
0.618法是根据黄金分割原理设计的,所以又称之为黄金分割法。 (0.618 method is based on the design principles of the golden section, it is also known golden section method.
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- 2011-12-17 19:45:45下载
- 积分:1
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Minimum-Risk-Bayes-classifier
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 )
- 2012-02-02 20:37:04下载
- 积分:1