Neural Networks ，8种神经网络，BP，RBF，CPN等通过VC++来实现了！

使用BP网络进行模式识别,识别0－9的数字-Using the BP network pattern recognition, identifying the number of 0-9

蚁群算法,蚁群算法原理及其应用中的源代码-Ant algorithm, ant colony algorithm and its application in source code

很好的遗传算法库，并且有详细的说明文档，Visual 2003环境下编写-A good genetic algorithm library, and a detailed description of documents, Visual 2003 environment to prepare

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.

遗传算法求解函数极值，一个基础的实用程序-Extremal function of genetic algorithm, one based on utility

这是一个使用VC++编写的“基于神经网络方法的专家系统源代码”，很有参考价值，请检查-It is a preparation of the VC, "based on neural network expert system source code," was useful, please check

SVDD的工具箱，可以很好的处理一类分类问题，详见Support Vector Data Description一文-SVDD tools，can be used to process one class classification problem,for more details ,we refer to "Support Vector Data Description"

从其它网站上下载的用VC++编制的模拟退火算法求解TSP问题的源代码-from other websites downloaded VC simulated annealing algorithm for TSP source code!

提出了基于最小化估计的泛化误差界非优化估计方法。目前大多数的核参数选择方法都是通过极小化了来得到最优参数值，但是求解优化问题的计算代价相当的大，并且不能很好地体现数据的分布特征。本文采用非优化技术，通过极小化泛化误差来优化核及相关参数，由于直接计算最小半径和最大间隔，避免了对优化问题的直接求解，因此可以很好地降低计算代价。并且该方法直接从样本出发，可以很好地体现数据的分布特征，不管数据分布是否均匀都可以适用。给出了基于凸包估计的SVM核选择的模型及实现算法。 -Proposed based on minimizing the estimated generalization error bound of the non-optimal estimation method. Most of the nuclear parameter selection methods are to get the best by minimizing the parameter values, but the computational cost for solving optimization problems is quite large, and can not properly reflect the distribution characteristics of the data. In this paper, the non-optimization technology, by minimizing the generalization error to optimize the nuclear and related parameters, due to the direct calculation of the minimum radius and maximum interval, avoiding the direct solution of the optimization problem, it can very well reduce the computation cost. And that the method directly from the sample one can well reflect the distribution chara

一个基本遗传算法求函数最大值的程序，在Vc中运行结果很好，有详细的代码注释，可以做为模板使用。 sag_sag.c-a basic function of genetic algorithms for maximum procedures, the Vc running good results, a detailed source notes, can be used as a template. Sag_sag.c