ZF

hi,this file implementation GUI with matlab

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))

license plate segmentation and recognition program on matlab plateform

MFCC参数提取的所有程序，已接近人耳听觉的特点在语音识别领域得到广泛应用(MFCC parameter getting)

an article about a model of dvr

zernike moment with matlab

machine learning中最经典的图书(The most classic machine books learning)

Forward Backward Propogation in Information Form

Fixed-point for functions of several variables (Function fixed_point_systems(x0, N) approximates the solution of a system of nonlinear equations F(x) = (f1(x), f2(x), ..., fn(x)) = 0 rewritten in the fixed-point form x = G(x) = (g1(x), g2(x), ..., gn(x)). This is done by the user at the bottom of the file. The sub-function G(x) has to be updated accordingly with the given system. The iteration scheme x(k+1) = G(x(k)) is implemented until condition |x(k)- x(k-1)| < tol is met, starting with an initial approxiamtion vector x0. If, after N iterations, the stopping criterion is not reached, a message concerning the iterated x s is displayed. the output consists of iterates of each variables x1, x2, ..., xn in each column. )

说明：  matlab 编写的gps/sins组合导航程序,附程序说明与数据输入文件(matlab prepared gps/sins navigation procedures, with explanation of the procedures and data input file)