SVM分类器，由上海交通大学编写，软件使用方便、好用

回溯法求01背包问题,c语言版本，由于时间关系没有将代码进行很好的整理，所有代码都在一个文件中-Retrospective Method 01 knapsack problem, c language versions, due to time not finishing a very good code, all code in one file

国内的一个A*算法演示，可以自己画地图，演示效果非常好-domestic A* algorithm for a demonstration, can you paint maps, the effect was very good demonstration

这是一个解决TSP问题的遗传算法.里面有详细的介绍如何编写遗传算法,很值得参考.-This is a solution of TSP genetic algorithm. Which is described in detail how to prepare genetic algorithm, very good reference.

介绍了根据三菱FX系列PLC编程口通信技术的特点,设计基于VB的PC与PLC机械手远程 控制程序.通过机械手监控系统软件的具体应用,系统地介绍了VB环境下的MSComm通讯控件 ,PLC与上位机的编程口通信协议,系统控制方法设计以及监控软件的实现过程.-Introduced acts according to Mitsubishi FX the series PLC programming mouth communication the characteristic, designs based on VB PC and the PLC manipulator remote control procedure. Through the manipulator supervisory system software s concrete application, introduced systematically VB under the environment MSComm communication controls, PLC and the

一个用vb编写的一维模糊控制器程序! 简单易懂-Vb prepared using a one-dimensional fuzzy controller program! Easy-to-read

一个bp实现回归的nn程序，可以从中学习神经网络在回归中的应用-Bp realize a return nn procedures, neural networks can learn from the reunification of the application

有指导的马尔可夫随机场（MRF）的图像分割代码，是有指导的方式（即用鼠标框出分类样本区域）。采用OpenCV编写。-Has guided Markov Random Field (MRF) image segmentation code, is guiding the way (that is, a classification of the box with the mouse sample region). Prepared using OpenCV.

一个进行数据挖掘的好方法，可以对数据集中的频繁项进行挖掘。但是有一定的缺点，还有需要改进的地方！-Data mining of a better way to focus on data mining of frequent items. Must have the disadvantage, however, also need to improve the place!

企业数字神经网络功能开发文章，能进行BP等神经网络的设计-enterprise digital neural network function development articles, BP will carry out other neural network design

最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it.