高斯混合模型算法

人工智能学习的重要内容，对加强和巩固所学内容将起到非常重要的作用。-AI an important part of learning, to strengthen and consolidate the contents will play a very important role.

人工智能中九宫问题的实现，初始数据随机生成，最终状态随机生成，并用图形界面显示搜索过程-AI JiuGongTu the realization of the initial data randomly generated, the final state of random generation, and using a graphical interface shows search process

本人编写的GramSchmidt神经网络算法，和传统的神经网络算法比较，该算法能自动产生神经元，节约时间，速度快！ 本人还加了自动产生分类的方法和适合各种函数的转换，该zip包括两个文件，一个是GramSchmidt正交分解，是个子函数，主函数是orth_GramSchmidt，放在同一目录下，就可运行，可以对benchmark的逼近和分类问题提供解决方案-I prepared GramSchmidt neural network algorithm, and the traditional neural network algorithm, the algorithm can automatically generate neurons, saving time, fast! I also added a method of automatically generated classification and conversion for a variety of functions, the zip includes two documents, one is GramSchmidt orthogonal decomposition is a sub-function, the main function is orth_GramSchmidt, on the same directory, you can run can benchmark approximation and classification problems to provide solutions

基本遗传算法,用来求解函数的最大值!在VC和TC下均可实现.-basic genetic algorithm used to solve the function of the maximum! And the VC can be achieved under TC.

基于BP算法的贝叶斯网络参数学习， 实现了图形话的节点表示和样本生成， 图形话的结果分析-BP algorithm based on Bayesian network parameter learning, the achievement of the graphics, then the node express and sample generation, if the results of graphical analysis

遗传算法求解TSP问题，内附有遗传算法基本原理，程序已验证-Genetic Algorithm for TSP problem, containing the basic principles of genetic algorithm has the process had been made to verify

用c＋＋写的用于FPGA设计中布图布线的工具源码，有几万行程序，包含文档-using c++ to write for FPGA design the layout of the wiring tools source, there are tens of thousands of procedure, the document contains

一个用C编写的SOM（自组织特征映射）神经网络源代码。-a C prepared by the SOM (self-organizing feature map) neural network source code.

遗传算法的源程序，里面有基本的遗传算法的各种操作过程，很全面！-genetic algorithm source, there are basic genetic algorithm various operational and very comprehensive!

以黎曼几何为理论依据，基于S.Amari的修正核函数思想提出了两种新的保角变换，用其对核函数进行数据依赖性改进，进一步提高支持向量机分类器泛化能力。以人工非线性分类问题 为对象进行研究，仿真实验结果表明采用新保角映射可以快速显著地改善分类器泛化性能，而且能大幅度地减少支持向量的数目。-Two novel conformal transformations were proposed based on the Riemannian geometry theory and S.Amari’sidea.And the kernel function was modified by the transformation in a data-dependent way.Our experimental results for theartificial nonlinear data set show that the generalization performance of support vector machines classifier is improvedremarkably and the number of support vectors is decreased greatly.