Statistical learning theory is based on a more solid theoretical basis, in order...

HTK系列的，参考资料，应该挺有用的，大家自己看看吧！-HTK Series, reference should be quite useless, we all see!

HIGHLIGHT TRUE #define NOHIGHLIGHT FALSE #define UPDATE TRUE #define NOUPDATE FALSE #define FORMAT TRUE #define NOFORMAT FALSE #define LEFT 0-HIGHLIGHT# define NOHIGHLIGHT TRUE FALSE UPDATE# define TRUE## define FALSE de NOUPDATE fine FORMAT# define TRUE FALSE# defin NOFORMAT e LEFT 0

宽度优先搜索法见cpp1.cpp文件，在vc6.0环境运行实现-width priority search see cpp1.cpp documents in the operation to achieve environmental vc6.0

bp人工神经网络算法实现，用java编写程序代码，应用案例是港口吞吐量预测-bp artificial neural network algorithm, using java programming code, the application case is the throughput of the port prediction

Biome是一个c++库，它的目的是个人_基于代理的模拟器（象 Swarm 或EcoSim）。它试图尽可能达到高效并且同时有好的设计-PROJECT is a c library, it is aimed at individual _ Agent-based simulator (like Swarm or EcoSim). It seeks to achieve efficient as possible while it is good design

在vc上运行的回答集求解器源代码，能够供在人工智能上学习的学习者使用-Running in vc answer set solver source code, for use in artificial intelligence to learn the use of the learners

双向广度优先搜索算法法见8SM2.cpp文件，vc6.0环境运行实现-two-way breadth- first search algorithm see 8SM2.cpp France, achieving operating environment vc6.0

以一个简单的例子说明模拟退火算法的思想。 模拟退火法求函数f(x,y) = 5sin(xy) + x^2 + y^2的最小值，对理解模拟退火算法是一个很好的程序示例。-to a simple example illustrates the simulated annealing algorithm thinking. Simulated Annealing for the function f (x, y) = 5sin (xy) x ^ 2 y ^ 2 minimum, the right understanding of simulated annealing method is a good example of the procedure.

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.

基于svm的机器学习文本分类方法，具有很好的借鉴意义-Svm-based machine learning text classification methods, with a good reference