VSM

利用贝叶斯实现的分类器小程序，显示出按照最小风险和最小误差分类的结果(Bayesian classifier implemented using a small program to show in accordance with the minimum risk and minimum error classification results)

make a pure tone with gaussian noise, this program show the minimun sampling frequency to not produce alliasing...

说明：  MATLAB短时傅里叶变换和morlet小波变换(short-time Fourier transform and morlet wavelet transform based on matlab)

利用matlab进行多项式的逼近和RBF网络函数的逼近，并分析其不同(Using matlab for polynomials RBF network function approximation, and to analyze the different)

说明：  基于matlab的牛顿拉弗逊法电力潮流计算(Based on The matlab Newton Laffer Johnson law power flow calculation)

Modeling a DC Motor in Stateflow® This is a model of a permanent magnet DC motor. The mode logic and dynamics of the DC motor are both modeled using Stateflow.

k-fold svm which downloaded before.

dubly feed induction generator with pwm inverter supply simulink matlab file

插值法的matlab实现，内含说明。拉格朗日插值，均分牛顿插值，差分牛顿插值，三次样条插值，埃米尔特插值(Matlab interpolation method implementations, containing instructions. Lagrange interpolation, Newton interpolation sharing, differential Newton interpolation, cubic spline interpolation, Amy Stewart interpolation)

In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is introduced by using the SVM. The kernel mapping idea is used to derive the non-linear version, Kernel Discriminant via Support Vectors (SVKD). In SVDA, only support vectors are involved to obtain the transformation matrix. Thus, the computational complexity can be greatly reduced for kernel based feature extraction. Experiments carried out on several standard databases show a clear improvement on LDA-based recognition