我的八数码游戏！！ 学人工智能的编写的

在各种数据源的分析中，收集数据非常重要，本算法提供了一种理论上可以保证性能的聚类算法并提供的相关证明。-Now kneel bowl beat Gou Tai Xu Pleiades cavity中， receive Moreton basin quasi-grind are linked to reset lynx ，算Yang Gou feed leftover braise instance, you can upset Article Sinopodisma殿cavity Abduction Chu Chun Kui Yang算sweet feed cavity gaze intently Shouson殿Longde 。

基于小波和神经网络的论文，来自期刊网-wavelet-based and neural network papers from the Journal Network! !

神经网络模式识别及其实现，第九章。 内含：HOPFIELD和LAM -pattern recognition and neural network to achieve, chap. Intron : LAM and HOPFILED

用感知器分类算法和最近邻分类算法对三类点进行分类，具有窗口界面，用鼠标点击就可以了，程序自动给出分类界面-Classification algorithm with the sensor and the nearest neighbor classification algorithm to classify three categories of points, with the window interface, with a mouse click can be had, the program automatically gives Classification Interface

介绍 ROBOCUP 3D SERVER的一些资料，网少比较少的，有兴趣的朋友可以研究看看。-ROBOCUP 3D SERVER introduce some of the information, the relatively small network of small, interested to see if a friend can be studied.

遗传算法中的新的多点交叉算法用来优化选择最优值-Genetic algorithm in the new multi-point crossover algorithm

包括883分酒问题，图的遍历等人工智能基本算法-Including 883 hours of liquor problem, artificial intelligence, such as graph traversal algorithm

关于人工鱼群算法的一个实例，可以实现所有的人工鱼的基本功能，是采用c开发的-Artificial fish-swarm algorithm on an instance, you can realize all the basic functions of the artificial fish is developed using c

基于神经网络的电离层F2层临界频率预测方法 基于神经网络的电离层F2层临界频率预测方法-Based on neural network of ionospheric layer F2 critical frequency forecasting methods based on neural network of ionospheric layer F2 critical frequency forecasting methods

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.