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HuffmanAlgorithmProbabilityTree
huffman tree in matlab
- 2013-09-07 05:44:48下载
- 积分：1
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Prueba
Is a good way to use
geodesic active contours give you more comfortable
you will be able to apply this code around the world
- 2013-10-04 16:44:13下载
- 积分：1
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cA-sand
元胞自动机沙土模型，matlab仿真代码，非常有参考价值。能成功实现。(The cellular automata sand model matlab simulation code, very valuable reference. Can be successfully realized.)
- 2012-09-13 11:50:39下载
- 积分：1
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MATLAB-Interface-Guide
书籍《MATLAB应用程序接口用户指南》主要介绍了matlab与c、c++、fortran等程序的混合编程以及matlab网络化编程知识(Books " MATLAB Application Program Interface User' s Guide" introduces the matlab and c, c++, fortran other procedures matlab mixed programming and network programming knowledge)
- 2013-05-25 14:23:04下载
- 积分：1
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Matlab-programming-code-optimization
matlab程序编写中代码优化的基本原则和方法，本文是一篇外文文献，其中举了一些实例(Matlab programming code optimization in the basic principles and methods This article is a foreign language literature, which gave some examples)
- 2012-06-27 17:25:58下载
- 积分：1
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mathematics-problem-solving
《基于MATLAB的高等数学问题求解》 随书附带源程序完整版( Higher mathematics problem solving based on MATLAB with the book comes with source program)
- 2015-12-20 21:12:04下载
- 积分：1
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Processcontrolengineeringandsimulation
过程控制工程及仿真--基于MATLAB/Simulink (Process control engineering and simulation- based on MATLAB/Simulink)
- 2010-12-09 10:26:18下载
- 积分：1
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S
说明： s函数编写指导的书，包括几个不错的例子。对入门的学习很有帮助(s function is to guide the preparation of the book, including several good examples. Very helpful for getting started learning)
- 2010-03-05 16:05:42下载
- 积分：1
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IsomapR1
说明： 一种经典的流形学习算法，IsomapR1，用matlab与c++混合编程实现(A classical manifold learning algorithms)
- 2010-04-13 08:59:30下载
- 积分：1
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NewK-means-clustering-algorithm
说明： 珍藏版，可实现，新K均值聚类算法，分为如下几个步骤：
一、初始化聚类中心
1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。
2、用前C个样本作为初始聚类中心。
3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。
二、初始聚类
1、按就近原则将样本归入各聚类中心所代表的类中。
2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。
三、判断聚类是否合理
采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps:
A, initialize clustering center
1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center.
2, with former C a sample as the initial clustering center.
3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center.
Second, initial clustering
1, according to the sample into the nearest principle clustering center represents the class.
2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class.
Three, judge clustering is reasonable
Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)
- 2011-04-06 20:45:56下载
- 积分：1
