C4_5

对回波信号实现仿真 及其不同方法的对比 对线性调频信号的仿真等。(Realize the simulation of linear frequency modulation signal compression And the echo signal simulation, comparison of different methods of implementation. Listen Read phonetically Dictionary- View detailed dictionary )

基本最小二乘估计递推算法matlab实现及绘图，以及生成M序列(Basic recursive least squares estimation algorithm and M series)

用于读取BVH文件,可以用来提取关键帧和分割(read BVH)

使用了JIT的高效的k均值算法，因为使用了JIT加速，所以在统计工具箱下，比一般的k均值要快许多(Efficient K-Means using JIT (Matlab code) This code uses the JIT acceleration in Matlab, so it is much faster than k-means in the Statistics Toolbox. It is very simple and easy to read. )

适用于三维的快速傅里叶变换的MATLAB程序(3d NUFFT)

可以进行直方图计算，也可以进行灰度运算，可以实现图像的变换操作(Histogram can be calculated, it can be gray-scale operation, the image transformation operation can be achieved)

费希尔线性判别分析代码 Find the Fisher linear separator w (a column vector). X is is the training set (X is a matrix. Each row of X is a vector containing the features of a single sample). y is a column vector with the labels of the training set (1 and -1). alg is a number between 1 and 3 that specifies how to find w: alg = 1: w is the generalized eigenvalue of A,B. alg = 2: w is the solution to Bw = (mu_1-mu_2) alg = 3: solve the corresponding regression problem( Find the Fisher linear separator w (a column vector). X is is the training set (X is a matrix. Each row of X is a vector containing the features of a single sample). y is a column vector with the labels of the training set (1 and-1). alg is a number between 1 and 3 that specifies how to find w: alg = 1: w is the generalized eigenvalue of A,B. alg = 2: w is the solution to Bw = (mu_1-mu_2) alg = 3: solve the corresponding regression problem)

matlab monte-carlo simulation for American option pricing and other path dependent derivative(matlab monte-carlo simulation for American option pricing and other path dependent derivative)

线性回归问题，内有简单matlab程序（可运行）以及PPT详解。(Linear regression, there are simple matlab (runs) and PPT explain.)

DNN程序，可以利用MATLAB实现深度神经网络。包含子程序(DNN program, you can use MATLAB to achieve a deep neural network. Include subroutine)