Java编写的分形程序，利用随机分形画出的蕨类植物的叶子图。另：有C源程序...

基于改进SVM的特征选择，可以帮助大家认识SVM-To improve the SVM-based feature selection can help you understand the SVM

一个机器人信息显示系统，代码比较全，你会喜欢的-Information display system of a robot, code-wide comparison, you would like

cost存放了一个强连通图的边权矩阵，作为一个实例。 可在workspace中加载 运用此算法要注意多次试验。 bianquan.m文件给出了一个参数实例，可在命令窗口中输入bianquan,得到邻接矩阵C和节点个数N以及一个任意给出的路径R,，回车后再输入jiaohuan3,得到了最优解。 由于没有经过大量的实验，又是近似算法，对于网络比较复杂的情况，可以尝试多运行几次jiaohuan3,看是否能到进一步的优化结果。-storage cost of a strongly connected graph edge weight matrix, as an example. Can be loaded in the workspace to use this algorithm to the attention of numerous tests. bianquan.m document gives examples of a parameter can be in the command window, type bianquan, be the adjacency matrix C and the node number N and an arbitrary given path R,, carriage return after the input jiaohuan3, have been the optimal solution . After the absence of a large number of experiments, it is approximate algorithm, for more complex network situations, you can try to run several jiaohuan3, can look to further optimize the results.

人工神经网络的c++实现源码,含实现神经网络层的类,神经网络的类,中文详细注释, 并含有使用示例,学习神经网络的好源码-Artificial neural network c++ implementation source code, including achieve the type of neural network layer, the type of neural network, a detailed English notes, and contain the use of examples of good learning neural network source code

游戏编程AI源码，包括A*等findpath的算法-AI game programming source code, including the A* algorithm, such as findpath

c++实现基于神经网络的空间大战，可以学习到如何利用神经网络进行游戏编程。-c++ implementation of space war based on neural networks can learn how to play games using neural network programming.

本程序实现通过构造一棵前缀树实现 最大模式频繁项集挖掘算法.应用fp树增长算法实现.-the program by constructing a prefix tree to achieve the greatest model Frequent Mining algorithms. Application fp growth tree algorithm.

贝叶斯算法是基于贝叶斯定理 P(H|X) = P(X|H)P(H) / P(X).。对于多属性的数据集，计算 P(X|Ci) 的开销非常大，为减低计算复杂度，我们做条件独立的假设，即给定元组的类标号，假定属性值有条件地相互独立，即在属性间不存在依赖关系。此程序仅为算法的一个实现，根据训练数据训练分类器-Bayesian algorithm is based on the Bayes theorem P (H | X) = P (X | H) P (H)/P (X).. For multi-attribute data sets, computing P (X | Ci) of the overhead is very large, in order to reduce the computational complexity, we do conditional independence assumption that a given tuple class label, it is assumed that property values conditionally independent of each other, that does not exist in the inter-attribute dependencies. This procedure is only an implementation of algorithm, according to training data classifier training

这是个解决八码问题的程序，是外国人编的，用了图形处理技术，请大家参考-This is a solution of 8 yards procedures, the series is a foreigner, his use of graphics processing technology, please refer to