Support vector machines (5) libsvm

经典的K均值分类算法源码，修正了原作者没有处理当类中样本为0的情况下的bug-classic K-mean classification algorithm source code, that the original author did not address when the category 0 samples of the bug

标准的粒子群优化算法,visual c++.net环境下编写的代码,有助于学习和理解粒子群算法-Standard particle swarm optimization algorithm, visual c++. Net code to prepare an environment to help learning and understanding of particle swarm optimization

Running this funciton each time to compute the LBP-TOP distribution of one video sequence. Reference: Guoying Zhao, Matti Pietikainen, "Dynamic texture recognition using local binary patterns with an application to facial expressions," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6):915-928.-It is 3D LBP, which is publised in IEEE TPAMI, a top journal of computer vision and pattern recognition. After unzipping the package, you can find a project coded in C++. After complied it, you can use it directly. Some tested images are also attached in the package.

用于multivariate时间序列分类，利用了weka机器学习系统（本站可下载）-for multivariate time series classification, the use of machine learning system weka (site available for download)

神经网络的算法，关于BP网络的编写-neural network algorithm, on the preparation of BP Network

非线性规划中求解无约束问题中的一个有效方法--模矢法(步长加速法),并用图表示出来步骤.-Nonlinear Programming Solution unconstrained problem of an effective method-- vector mode (step acceleration), and using map out steps.

自动地分类和聚集文档，可以选择作为一个Web服务。这个程序完全是用Java编写的，利用了 Weka机器学习工具包。-automatic classification and gathering documents, we can choose as a Web service. This procedure is completely written in Java, Weka use of machine learning kits.

C词法分析器实现,AOE网络算法实现,KRUSKAL算法实现,PRIM算法实现,计算机图形学影线填充算法（键盘坐标输入）,计算机图形学影线填充算法（鼠标输入）,人工智能A*算法实现的C语言程序-C lexical analyzer to achieve, AOE network algorithm, KRUSKAL algorithm, PRIM algorithm, computer graphics hatched filling algorithm (keyboard input coordinates), computer graphics hatched filling algorithm (mouse input), Artificial Intelligence A* algorithm The C Programming Language

this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the hidden states of a nonlinear, non-Gaussian state space model.-this demo is to show you how to implement a ge neric SIR (a.k.a. particle, the bootstrap. Monte Carlo) filter to estimate the hidden stat es of a nonlinear. non-Gaussian state space model.

用Fast BP实现复杂的函数逼近,对初学者有一定的帮助,是在BP的基础上增加了R.-Fast BP achieved with a complex function approximation, there is some help for beginners, is based on the BP increase in R.