遗传算法软件包，其中有例子配合理解，十分不错

科罗拉多大学开发的人脸识别系统,要在UNIX下运行.-developed at the University of Colorado, the face recognition system to run under UNIX.

Matlab implementation of ID3 and NaiveBayes classifier. It also includes example dataset as well.

这是一个神经网络的开发工具-This is a neural network development tools

蚁群算法的演示程序，很形象的说明的蚁群群体合作的智能性，很好的诠释了蚁群算法的精髓-Ant colony algorithm demo program, it is the image of the ant colony that cooperation between the intelligence groups, very good interpretation of the essence of the ant colony algorithm

微软的基于HMM的人脸识别原代码, 非常经典的说-Microsoft HMM-based Face Recognition source, said the very classic

对于具有一定规模的TSP问题的求解，至今没有较完善的求解办法。本程序是用遗传算法来求解TSP问题。-for a certain size of the TSP solution and there has been no better way to solve. This procedure is used genetic algorithms to TSP.

clementine8.1中文手册经典的数据挖掘软件说明-clementine8.1 Chinese classic manual data mining software Note

基于canny的灰色预测和模式识别,标定的一种做法是：在一块平板上画若干个点， 同时用这两部相机照相，分别得到这些点在它们像平面上的像点，利用这两组像点的几何关系就可以得到这两部相机的相对位置。然而，无论在物平面或像平面上我们都无法直接得到没有几何尺寸的“点”。实际的做法是在物平面上画若干个圆（称为靶标），它们的圆心就是几何的点了。而它们的像一般会变形，如图1所示，所以必须从靶标上的这些圆的像中把圆心的像精确地找到，标定就可实现。-Based on the gray canny prediction and pattern recognition, calibration approach is to: a plate painted in a number of points, at the same time with these two cameras camera, respectively, as these points in their plane as points, the use of these two groups geometric relationship between the image point can be that the relative positions of two cameras. However, in terms of objects such as plane or plane, we can not directly be no geometry point. Actual practice is complex plane painted in a number of round (known as targets), their geometric center of a circle is the point. And they generally like deformation, as shown in Figure 1, it must target these round, like in the center of a circle of like to find precise, calibration can achieve.

路由选择过程的c算法程序 与DSR 只是简单地使用最短路径不同，EMRPC 使用了更复杂的路由选择机制以实现多径路由和能量均衡。-process of routing algorithm c procedures and DSR is simply to use the shortest path different. EMRPC use of a more complex routing mechanisms to achieve multi-path routing and energy balance.

This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox