关键词: 支持向量机, 蚁群优化算法, 参数优化, 分类正确率

Abstract: SVM parameter selection determines SVM classification accuracy and generalization ability, and its lack of theoretical guidance parameter optimization, ACO-SVM model is proposed, it predicts the SVM classification accuracy as the objective function, and improves the ant colony algorithm, with the introduction of search and updates the pheromone based on time-varying function update policy, uses the ant colony algorithm parallelism, positive feedback mechanism and strong robustness, in order to achieve optimal goals and get the optimal combination of parameters of SVM. The results of numerical value experiments show that the improved Ant Colony Optimization algorithm for SVM parameters selection has better optimization performance and higher classification accuracy. This method has the better parallelism and strong global optimization ability.

Key words: Support Vector Machine（SVM）, Ant Colony Optimization Algorithm（ACOA）, parameter optimization, classification accuracy