bg_SingleGaussian

rgb to cieluv transformation

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

matlab 方差分析，很好的数学建模资料。内含数据和程序代码。(matlab Analysis of variance)

多目标跟踪时的逻辑航迹起始算法，用于跟踪航迹起始，仿真效果不错(Logical starting algorithm of multi-target tracking )

通过正交点的寻找从而用几何最小二乘拟合椭圆的方法(Finding points to the orthogonal least squares fitting using the geometric method of elliptic)

fingerprint autneication

localization error for wireless sensor network by Matlab

Mechanical vibrations exercises.

BASIC SOURCE SEPARATION CODE

基于Kalman滤波算法的自适应AR模型(Adaptive Kalman filtering algorithm based on AR model)