真正可以用的匈牙利算法 我校赛的时候用过 很不错 不过写的不够简洁...

一个*算法，有详细的描述过程，其中的奥秘自己看。

可视化的手写（鼠标）字母数字神经网络训练程序。-visulized neural network trainning programme。

自己用swarm开发的一个车辆跟驰的模型，希望能跟广大爱好者共同探讨。-own use development of a swarm of vehicles- following model, the majority of fans are able to explore together.

粒子群算法的示例程序，不错。可用来进行函数优化、神经网络训练等。-PSO algorithm sample program, yes. Available for function optimization, neural network training.

BP神经网络解决异或问题-BP neural network or problems to solve differences

A SVM classifier, with detail documents.

SPEA2源码，效果相当好 ZIZTLER系列复杂函数只有第三个效果不太好-SPEA2 source, the effect ZIZTLER very good series of complex functions, only the third effect is not a good

用粒子群优化算法计算给定图像的平滑度。

压缩包中的一系列matlab程序实现了“特定人群语音情感识别”这一新颖的研究课题。具体介绍请见压缩包中的论文。-compressed in a series of Matlab program of "specific emotional crowd voice recognition" of the new study. Specific details of compressed see the papers.

最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it.