本程序实现了多城市的tsp问题（即旅行商问题）的遗传算法

贝叶斯学习算法分类文本。基于朴素贝叶斯分类器的文本分类的通用算法，是目前所知文本分类算法中最有效的一类-Bayesian text classification algorithm. Based on the simple Bayesian classifier of the definitive text classification algorithm, which is known text classification algorithm the most effective one category

转载：对神经网络中使用最广泛的一种：BP神经网络采用vbmatlab来实现，把训练数据拷入，load的*.m文件中, 文件中部load的*.m为测试数据 save 存盘的文件为测试数据的bp预测值, 该程序可以扩充n个输入,n个输出,很容易-reproduced : the neural network the most widely used one : BP vbmatlab using neural networks to achieve, it is copyed into the training data. m*. load the document, Central to the paper load*. m to save Inventory test data for the paper test data bp predictive value, The procedure can be expanded to import n, n output, it is easy

遗传算法 算法实例MATLAB程序，包括了：选择 变异 交叉 遗传算子-GA

FP-GROWTH算法的源代码(JAVA)-FP-Growth code and algrithem

greedy em 混和模型训练算法-greedy em hybrid model training algorithm

ICA经典文章,入门必看。受益，高阶矩统计牲在信号处理中的应用，详细介绍许多重要概念。-ICA classic article, the entry Watchable. Benefit Higher Moments statistical offerings in signal processing applications, detailed many important concepts.

本程序是数据挖掘中的关联规则模型中著名的Aprior算法的VC实现程序，可用于知识发现、数据挖掘、人工智能、模式识别等领域（请先解压文件）-The code is the VC implementation of the well-known Aprior algorithm in Association Rule Model of Data Mining field, can be used in Knowledge Discovery, Data Mining, AI, Pattern Recognition and so on.(Please unzip the file first)

tsp问题，是一个很典型的NP完全问题，该程序用了遗传―退火结合算法，能优化计算-tsp problem is a typical NP-complete problem, the program is to use genetic- combined annealing algorithms that optimize computing

一个利用hough变换来检测图像中直线的函数的源代码-using a Hough transform to detect images linear function of the source code

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross- (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and ot