中国科学院李德毅院士在北京航空航天大学关于不确定性人工智能的讲座PPT。...

也是学习别人的程序，不过他没有把核心代码给我-also learn from others procedures, but he did not take me to the core code

ClustanGraphics聚类分析工具。提供了11种聚类算法。 Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean Proximity Centroid (UPGMC) Median (WPGMC) Increase in Sum of Squares (Ward s Method) Sum of Squares Flexible (ß space distortion parameter) Density (or k-linkage, density-seeking mode analysis) -ClustanGraphics clustering analysis tools. Provides 11 kinds of clustering algorithms. Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean ProximityCentroid (UPGMC) Median (WPGMC) Increase in Sum of Squares (Ward s Method) Sum of SquaresFlexible (? space distortion parameter) Density (or k-linkage, density-seeking mode analysis)

svm training example from svm.org

用高斯和离子滤波来跟踪视频图像中的移动对象，和模式识别有关-And ions with Gaussian filtering to track video images of moving objects, and pattern recognition on the

改进遗传算法－郭涛算法做最优化问题很管用,算法的基本思想是 先任意产生n个随机数，然后从n个数里随机选择m个数，再有这m个 数合成一个新数，将这个新数同n个数中间适应值函数值的最差的比较， 如果好的话就取代最差的那个，如果它比最好的还要好的话，则把最好的 也取代。如果比最差的坏，则重新合成一个新数。依次循环下去。 程序的奇妙之处是GA_crossover()函数，产生的新数确实比较好，看看 那位大侠能改进一下，产生比这跟好的数。-improved genetic algorithm- Guo Tao done optimization algorithm is very effective, and the algorithm is the first basic idea arbitrary n generated random number and then the number li n m randomly selected number, and this number m of a new synthesis of the new middle with n number fitness function of the worst by comparison, if a good case to replace the worst, um, if it even than the best, then ruled the best have replaced. If worse than the worst, a de novo synthesis of new. Followed by the cycle continues. The magic of procedures is GA_crossover () function, the new really good to see heroes who can be improved upon, it generated more than a few with a good.

另一个足球机器人程序，有非常好的参考价值-Another robot football program, there are very good reference value

LS-SVM_UserGuide是LS-SVM使用方法介绍的一篇文章，竟对我们学习支持向量机有很大的帮助。-LS-SVM_UserGuide is the use of LS-SVM method described in an article we had on the support vector machine learning of great help.

This demo nstrates the use of the reversible jump MCMC simulated annealing for neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL

solve the 8-puzzle problem using A* algorithm. Definitely written by my self, also include BGI graphics library output.-solve the 8-puzzle problem using A* algori thm. Definitely written by my self. NBS also include graphics library output.

遗传算法求解TSP问题-genetic algorithm for TSP