基于蚁群算法的支持向量机参数选择方法研究

支持向量机（SVM）的参数取值决定了其学习性能和泛化能力。对此，将SVM参数的选取看作参数的组合优化，建立组合优化的目标函数，采用蚁群算法（ACA）来搜索最优目标函数值。ACA是一种优化搜索方法，具有较强的鲁棒性、优良的分布式计算机制。仿真表明，ACA是选取SVM参数的有效方法，应用到函数逼近时有优良的性能。

Parameters Selection of Support Vector Machine Based on Ant Colony Algorithm

The learning performance and generalization ability of support vector machine(SVM)are de- pendent on its appropriate parameters selection.Thus,the parameters selection problem of SVM could be considered as a compound optimization problem by setting the objective function.The ant colony algo- rithm(ACA)was applied to search the value of optimal objective function with the fine performance of robustness and distributed computing.The simulations results show that ACA is an effective method for selecting parameters of SVM,and can obtain fine performance for the function approximation.