HopfieldnetC++source，Hopfield网络的原代码。C++代码

神经网络模式识别及其实现，第四章。 内含：ALOPEX和BACKPROP程序。-pattern recognition and neural network to achieve, the fourth chapter. Intron : ALOPEX BACKPROP and procedures.

用遗传算法求解最短路径问题-Genetic Algorithm for the shortest path problem

数据挖掘中的关联规则发现算法-Data Mining Association Rules algorithm

模式识别以及重要问题，很好的内容，千万不要错过-Pattern recognition, as well as important issues, very good content, do not miss

pso 的一个演示源码，欢迎初学者下载，共同进步-pso source of a presentation, please download beginners, and common progress! ! ! ! ! !

这个程序是个神经网络提供学习能力的实例，是前一个simpleann的功能加强版。-this procedure is a neural network learning ability example is the former simpleann an enhanced version of the function.

遗传算法，通过选择、交叉、变异求取函数最优解c程序简单实现，并拥有最优保存思想。-Genetic algorithm, selection, crossover and mutation to strike a function of the optimal solution c procedures are simple to achieve, and has saved the best idea.

神经网络程序代码，小小初学，不知甚多，望多多指教。-Neural network program code, little beginner, I do not know a lot, hope the exhibitions.

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

遗传算法VC编程，对于初学者可能有用，多多支持~-VC programming genetic algorithms, may be useful for beginners, the generous support of ~