本程序代码是人工智能中九宫搜索的解决 采用Java语言

统计学习理论是建立在一套较坚实的理论基础之上的，为解决有限样本学习问题提供了 一个统一的框架。它能将很多现有方法纳入其中，有望帮助解决许多原来难以解决的问题（比 如神经网络结构及参数选择问题）；同时。在这一理论基础上发展了一种新的通用学习方法： 支持向量机（Support Vector Machine，SVM）。它己表现出很多优秀的性能，并已经成为当 今机器学习领域的研究热点。-Statistical learning theory is based on a more solid theoretical basis, in order to solve the learning problems of limited samples provides a unified framework. It will be incorporated into many existing methods, which could help solve many of the original difficult to resolve problems (such as neural network structure and parameters selection) the same time. Developed on the basis of this theory, a new universal learning methods: support vector machine (Support Vector Machine, SVM). It has been shown a lot of excellent performance and has become the hotspot of machine learning research in the field.

人工智能经典问题TSP问题的C语言实现，内含源程序和调试过程生成的文件-AI classic TSP"s C language, and includes source code debugging process produces files

利用遗传算法求解TSP的源代码程序 可以精确求出50城市-The use of genetic algorithm to solve TSP program source code can be precisely calculated 50 cities

基于主成份分析（PCA）的人脸特征识别核心源程序。

这是一个国外的Hopfield神经网络，有实例，可修改！-Hopfield Networks

k中心点 编制和调试一个程序，它将用户输入的正规式转换为以状态图和矩阵形式表示的确定有穷自动机。 1．把正规式转换为NFA 2．将NFA确定化为DFA • ＃作为正规式的终止符 • 考虑复合正规式 • 开始状态号为0 -focal point for the preparation of k and debug a program, it will the user to enter the formal conversion to a state diagram and matrix forms express the determination of DFA. 1. The formal type is converted to NFA2. Will determine the NFA into a DFA

用蚁群算法求解TSP（旅行商）问题，按照TSP.m中要求的格式输入程序参数即可运行！-Using Ant Colony Algorithm for TSP (traveling salesman) problems, in accordance with the format requested TSP.m input process parameters to run!

DBSCAN Designed by Dr. Michal aszykowski Department of Chemometrics Institute of Chemistry, The University of Silesia. It s the basic one.

apriori经典算法及一个改进算法的程序,里面有具体的解析与操作-classical apriori algorithm and an improved algorithm for the procedure, there are specific analytical and operational

是MATLAB的RBF神经网络建模程序，比较有参考价值阿，需要的就下吧-MATLAB is the RBF neural network modeling procedures, more A reference value, we need it on the next