Code-nonlinear-equa

画电偶极子等势面，电偶极子的三维等势面，可以改变x，y值(Painting equipotential surface dipole)

abe model matlab very good

在最小二乘参数估计值的基础上，引进补偿项σW2C-1D Ø 0，则获得了参数的无偏估计。针对模型噪声来说，RCLS算法的适应能力比RLS更好。(In the least squares estimation parameters, on the basis of introduction of compensation term σ W2C- 1 d Ø 0, then get the parameters of the unbiased estimation. According to the model for noise, RCLS algorithm adaptive RLS better than.)

LUCAS-KANADA仿射光流计算的例子(LUCAS-KANADA)

基于DQPSK差分编码调制解调,希望对你有帮助。(DQPSK modulation and demodulation based on differential encoding, I hope to help you.)

Incremental Principal Component Analysis

Matlab下单字语音识别程序。Readme.txt中有使用方法的详细说明。(Vocabulary speech recognition under the program Matlab. Readme.txt in detail using the method.)

Matlab code for for Fourier Transform

matlab图像处理命令，集中了matlab图像处理函数,对初学者很由帮助.(Matlab image processing orders, concentrated Matlab image processing function, right from the very beginners to help.)

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