How-to-display-image-in-GUI-using-Matlab-_-About-

该程序在matlab环境下完成抽样量化编码功能（非均匀量化）。(err)

说明：  目标匹配经典特征shape context的Matlab源码！(The source code of the shape context!)

pca做特征降维，然后进行特征空间随机分割构造多个svm分类器，并行处理，对样本进行分类，基于特征空间的svm多分类器(using pca reduce feature dimension,split feature space and then randomly divided over svm classifier construction, parallel processing, the samples were classified, based on multi-feature space classification svm )

关于matlab主元分析的一个简单单易懂的实例，可供初学入门者参考学习。 (On the matlab principal component analysis is a simple single-understand examples, for novice beginners for reference.)

关于凸优化资料，目前最新版本的matlab图优化工具加载方法和学习算例。里面讲述了多目标优化、内点法等一些凸优化具体算例，希望对大家有用。(About convex optimization data, the latest version of the loading method matlab figure optimization tools and learning examples. Which describes a multi-objective optimization, interior point method and some convex optimization numerical example, we want to be useful.)

十四单元的matlab程序，有少许改动。(14 unit matlab procedures, there are little changes.)

利用熵值法判断某个指标的离散程度的。在信息论中，熵是对不确定性的一种度量。信息量越大，不确定性就越小，熵也就越小;信息量越小，不确定性越大，熵也越大。根据熵的特性，我们可以通过计算熵值来判断一个事件的随机性及无序程度，也可以用熵值来判断某个指标的离散程度，指标的离散程度越大，该指标对综合评价的影响越大。 因此，可根据各项指标的变异程度，利用信息熵这个工具，计算出各个指标的权重，为多指标综合评价提供依据。(Using entropy method to judge the degree of dispersion of a certain index.)

一款很不错的MATLAB优化视频教程，讲述详细，希望用得着的快快分享(A very good video tutorial of MATLAB optimization, described in detail, I hope to share useful for quickly)

说明：  QPSK的Matlab实现 使用伪随机序列，低通滤波，可看图(QPSK Matlab using the pseudo-random sequence, low-pass filtering, flashcards)

will load a weight and classifier matrix. It will start training process.