神经网络模式识别及其实现，第四章。 内含：ALOPEX和BACKPROP程序。...

模拟退火算法：源于遗传算法，用来解决优化、决策等复杂的模型和问题。-Simulated annealing algorithm: from the genetic algorithm, to solve optimization, decision-making complex models and problems.

1、该工具包用于安装Lm和RBF两个神经网络工具包 2、直接运行LmNet.exe或_install.bat即可； 3、完成成功后，请将 binwin32 目前加至系统的 path中。 具体为：我的电脑-->属性-->环境变量-->将“binwin32”加至path路径中。-1, the tool kits for the installation and Lm 2 RBF neural network tool kits 2, or directly running LmNet.exe _install.bat can; Three complete success, I would be grateful if bin win32 increase to the current system which the path. Specifically : my computer-- gt; Attribute-- gt; Environment variables-- gt; "Bin win32" to increase path PATH.

通过遗传算法进行金融预测的一篇英文的好文章。-Through the genetic algorithm to conduct financial forecast of a good article in English.

c++编译的目标规划遗传算法，编写了较为详细的原程序，被广泛使用。-the genetic programming algorithm, to prepare a more detailed program, has been widely used.

变色龙层次聚类算法，利用动态模型的层次聚类算法-chameleon hierarchical clustering algorithm, using a dynamic model of hierarchical clustering algorithm

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

用VISUAL C＋＋编程实现指纹图像的特征提取以及对指纹图像的识别-Utilising VISUAL C++ to make programs, we can get the characters of image and identify the image of finger mark

用于程序优化与遗传算法-procedures for optimization and genetic algorithms

经典算法应用：神经网络算法的一个实际应用-Application of the classical algorithm: neural network algorithm, a practical application

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.