matlab

通过一个图像序列来创建背景图像，用于背景差分(Through an image sequence to create a background image for the background subtraction)

1个matlab代码为机器人提供PID控制器。只要robot.m运行它。cinematic.m中描述机器人的电影 (A matlab code for the robot to provide PID controller. Robot.m long run it. cinematic.m description in robot movie )

SVDGUI演示了计算任何实矩阵的奇异值的步骤(SVDGUI Demonstrate the computation of the SVD. SVDGUI(A) shows the steps in the computation of the singular value decomposition of any real matrix.)

c4.5经典算法，实现决策树分类功能，可以对连续数值和离散数值实现很好的分类，并有剪枝功能(c4.5 classic algorithms, to achieve the decision tree classification, can be continuous and discrete numerical values to achieve good classification, and a pruning function)

说明：  复杂网络中的GN算法...............(Complex network of GN algorithm ...............)

说明：  实现磁盘级文件读写，穿透还原。实现磁盘级文件读写，穿透还原。(Achieve disk-level file reading and writing, through reduction. Achieve disk-level file reading and writing, through reduction.)

说明：  给出RS编码和译码的原理 以及解决方案，特别是仿真的一种解决方案，值得参考。(RS gives the principle of encoding and decoding as well as solutions, in particular the simulation of a solution, it is also useful.)

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

很好的Adaboost自学实例，简单易懂(A simple but robust Adaboost exmaple, which is easy enough by self-learning)

puma robot sim using matlab gui