遗传算法在matlab中的实现

The Matlab Toolbox for Pattern Recognition

好不容易找到的外国博士论文,关于Text-independent speaker recognition-Hard to find foreign doctoral thesis on the Text-independent speaker recognition

数据挖掘k-measextions 算算法，改进了其中部分结构。-Data Mining k-measextions counting algorithm, to improve some part of the structure.

与神经网络有相似的根源。但近年来在分类中得到了广泛的应用。这意味着，如果我对一些事物进行了分类（但你对我如何分类一无所知，或者说你不知道用于分类的规则），当一个新数据出现时，SVM可以预测它应该属于哪个集合。

基本蚂蚁算法及其改进算法求解NP问题的论文汇总，而且全是中文，对于初学者一定有帮助-The basic ant algorithm and its improved algorithm for solving NP problems summary papers, and they are all Chinese, will certainly be helpful for beginners

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

通俗的介绍了RVM与SVM的区别,写的非常通俗易懂-Tutorial of RVM

该算法经运行，无任何问题，可用于解决遗传算法求解车辆路线问题-The algorithm by running without any problems, can be used for genetic algorithm to solve Vehicle Routing Problem

利用PSO优化SVM，利用分组式训练方法提高算法速度-PSO to optimize the use of SVM, the use of packet-style training methods improve algorithm speed