TheFilterDesign

基因算法实现的特征提取，实现平台是matlab(feature selection with genetic algorithm)

纹理图像分割论文和代码，还算比较完整的，对做方面毕业设计的有点用！(Texture image segmentation papers and code are fairly more complete, to make the design a bit to use in graduate!)

粒子群算法、遗传算法优化RBF径向基神经网络(Particle swarm optimization, genetic algorithm optimization RBF RBF neural network)

Code to run the Non-negative Matrix Factorization algorithm as presented in the Lee, Seung 1999 Nature paper.

教程，很好的。很全面，而且容易懂，里面好多例程可以参考学习。看看就知道了，有更好的一起分享。。。。。。(Tutorials, very good. To see to know, better to share. . . . . .)

用matlab设计的四种fir滤波器，低通、高通、带通、带阻等四种滤波器(Matlab design four kinds of fir filter, low-pass, high-pass, band-pass, rejection and so on four kind of filter.)

this program will evaluate the fdtd solution for the scattering problem

变换编码中相关系数的求解，特别适用于KLT变换，得到对角矩阵(Correlation coefficients in transform coding solution, especially suitable for KLT transform, the diagonal matrix)

迭代解码算法Bit Flipping Algorithm，使用时先载入校验矩阵(Iterative decoding algorithm Bit Flipping Algorithm, using the check matrix when the first load)

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)