人工智能中经典算法

支持向量机中的JAVA程序 初学者可以方便使用-Support Vector Machine in the JAVA program for beginners can easily use

汉字识别系统，非常简单，网络上看到的 一起学学啊，对模式的一个简单匹配.-Chinese character recognition system is very simple, see the Web together to learn ah, the model of a simple matching.

程序包含的设置包括：列数、样本总个数、建模样本数、预报因子数、预报对象数、学习因子、 动量因子、训练次数、总体误差、隐节点数。-procedures contained in the set include : number, the total sample number, the number of samples modeling, forecasting several factors, forecasting several targets, learning factor, momentum factor, the number of training, the overall error of hidden nodes.

我自己编写的A*搜索算法，效率还是很不错的，推荐。-myself prepared by the A* search algorithm, the efficiency is still very good and recommended.

神经网络仿真工具，本程序是BP算法的演示程序, 其中的Levenberg-Marquardt算法具有实用价值.-Neural network simulation tool, this program is BP algorithm demo program in which the Levenberg-Marquardt algorithm has practical value.

使用线性分类器进行分类，采用感知器算法中的“奖惩算法”，各提取3类中的前25个样本共75个作为学习样本。-The use of a linear classifier for classification, perceptron algorithm using the reward and punishment algorithm , the extraction of 3 categories of the top 25 a total of 75 samples as the learning samples.

决策树分类C5.0算法的C语言实现源码！-C5.0 decision tree classification algorithm realize the C language source code!

A SVM classifier, with detail documents.

遗传算法 多目标的优化 是用GAOTV包做的-GA multi-objective optimization is done with the package GAOTV

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.