This is an image processing pattern recognition on the source code, including C...

神经网络的应用程序，里面实现有一定的价值，有较强的参考价值和利用价值-Neural network applications, which achieve a certain degree of value, have a stronger reference to the value and use value

k-means算法(matlab编写),其中包含测试数据集,可以使用.-This is a k-means algorithm(in matlab) contains data set which can be used

这是一个朋友做的人脸检测软件，是基于OpenCV 做的，很具有参考价值-This is a friend to do face detection software is based on OpenCV do, it is very valuable reference

人工智能的教材啊- Artificial intelligence teaching material

机器博弈机器搜索算法的研究，做acm与人工智能的可以参考下-Machine Game Machine search algorithm research, acm to do with artificial intelligence can refer to the next

模式识别中的CYK算法-pattern recognition algorithm for the CYK

清华大学不确定规划教材中的模糊规划求解算法1-Tsinghua University, uncertainty in the planning of teaching materials fuzzy programming algorithm 1

libsvm在matlab中的应用。把libsvm嵌入matlab中调用。-libsvm in matlab application. Embedded matlab to call libsvm.

文中设计了一个3层径向基神经网络(RBFN)用于对企业的5项评价指标进行聚类分析，并与蚁群算法做了比较分析。RBFN由输入层 到隐含层采用传统的K一均值算法，隐含层到输出层通过“模2递减”学习速率的BP学习；蚁群算法根据信息素的分配能够自动调整收索 路径，从而达到数据自动聚类的目的。结果表明，与蚁群算法相比，改进RBFN具有快速收敛、自动识别奇异样本的优点，而蚁群算法 无须教师学习，并能够达到全局最优。-In this paper, we designed a 3-layer RBF neural network (RBFN) for the 5-to-business evaluation indicators cluster analysis and ant colony algorithm has done a comparative analysis. RBFN from input layer to hidden layer using the traditional K-means algorithm, hidden layer to output layer through the Mode 2 decreasing learning rate of BP learning ant colony algorithm based on pheromone can automatically adjust the allocation of land Faso path, thereby to achieve the purpose of automatic data clustering. The results showed that compared with the ant colony algorithm to improve the RBFN has a fast convergence, automatic identification of singular advantage of the sample, while the ant colony algorithm do not need teachers to learn and be able to reach the global optimum.

是各种常用算法的集合，又递归、神经网络、遗传、枚举、排序、圆周率求解等等。是学习的好材料。-Is a collection of various commonly used algorithms and recursive, neural networks, genetic, enumeration, sorting, solving, etc. pi. Is a good learning materials.