GraphSeg

实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))

说明：  image processing and noise models

自编matlab小波分解以及重构的函数，希望大家多多指教，后期完善了自编的卷积积分再上传(matlab for wavelet by self)

可变模糊识别。利用可变模糊理论方法，对风险进行识别，并可以对风险因素进行重要性排序。(Variable Fuzzy Identification. Risk factors can be identified and sorted according to the importance degree based on the variable fuzzy theory.)

这是用于电火花机床加工控制参数优化的一个多目标遗传算法的matlab程序，自己原创，分享出来和大家交流。 (It is used for electric discharge machining control parameters optimization of a multi-objective genetic algorithm matlab program, their own originality, share them and everyone exchanges.)

用于2D图像的Encoder，Decoder(a JPEG Decoder for 2-D image,contain the jpeg file which needed)

copula的蒙特卡洛模拟，随机生成各种Copula函数。(simulate copula)

matlab_入門程式範例台灣大學張智星教授撰寫(matlab entry program examples)

Image Processing using matlab

MATLAB调用动态链接库实例程序，源代码(MATLAB example program called dynamic link library)