用MATLAB编写的支持向量机，实现了线性可分的…

此程序为训练RBF网权值的量子粒子群优化算法，供各位参考-this program for training RBF net weights Quantum Particle Swarm Optimization Algorithm for reference

这是个神经网络工具箱的一点MATALAB 程序-This is a neural network toolbox 1:00 MATALAB procedures

遗传算法的源代码，使用c++语言编写，结构清晰，注释完整，明了易懂-Genetic Algorithm

遗传算法分层遗传算法-GA GA stratified

微分进化算法DE（Differential Evolution）由Storn和Price等学者于1995年首先提出。它是一种基于种群优化的新智能优化方法，它已被证明在求解过程中具有高效性、收敛性、鲁棒性等优点-Differential evolution algorithm DE (Differential Evolution) by Storn and Price and other scholars in 1995, the first place. It is a species based on the optimization of the new intelligent optimization method, which has been proved in the solving process with high efficiency, convergence, robustness, etc.

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

微软的基于HMM的人脸识别原代码, 非常经典的说-Microsoft HMM-based Face Recognition source, said the very classic

蚁群算法的实现，使用一个例子验证蚁群算法的有效性。-ant algorithm implementation, use an example to verify the validity of ant colony algorithm.

遗传算法强制交叉算法-GA mandatory cross-Algorithm

本程序利用蚂蚁算法解决TSP（旅行商问题）问题-This procedure using ant algorithm to solve TSP (Traveling Salesman Problem) problem