svmTrain svm在matlab中运行 训练

这个程序是A*算法的简单实现，来解决八数码问题-It just simply applys the A* algorithm to solve the Eghit-puzzle problem

神经网络研究生收费模型的具体方案以及其应用-Neural network model of graduate student fees, as well as their application specific program

用人工神经网络的bp的方法解决异或问题-Using artificial neural network bp means to solve the XOR problem

改进的遗传算法，有兴趣的可以下-Improved genetic algorithms, are interested in can be done next

遗传神经网络-Genetic Neural Network

水平有限，仅供参考！望大家多提宝贵意见！-limited, and for reference purposes only! Hope you will provide more valuable opinions!

神经网络C程序的第10章-neural network C procedures for the Chapter 10

关联规则中的频繁项集生成算法Mafia1.4版本，一个效果非常好的算法，可在linux和windows下编译运行。-association rules of frequent item sets Algorithm Mafia1.4 version, a very good effect algorithm, and the Linux compiler running under windows.

用遗传算法求解背包问题是南京航空航天大学信息与计算科学专业编写的.本程序利用遗传算法来求解背包问题.采用二进制字符串编码,1表示选择物体,0则不选择. 背包问题描述:在M件物品取出若干件放在空间为W的背包里，每件物品的重量为W1，W・2……Wn，与之相对应的价值为P1,P2……Pn。求出获得最大价值的方案。注意：在本题中，所有的重量值均为整数。-genetic algorithm knapsack problem is the Nanjing University of Aeronautics and Astronautics, Information and Computing Science prepared. The procedure the use of genetic algorithms to solve knapsack problem. binary string encoding, an option objects, 0 no choice. Knapsack Problem Description : M items removed several pieces on the space-W backpack, Each of the weight W1, W 2 ... Wn, the corresponding value of P1, P2 ... Pn. Sought to gain maximum value of the program. NOTE : In this issue, all the weight values are integers.

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.