Abstract:Support Vector Machine (SVM) is an intelligent technology for classification problems. Because of its flexibility, computational efficiency and capacity to handle high dimensional data, SVM has become a popular research issue in recent years. Selection of optimal parameters is important for an SVM. The traditional methods, such as the k-fold cross validation, can select optimal parameters, but would take too much time. In this paper, a method of SVM parameter selection based on the bound of structure risk is proposed. First, the bound of the structure risk is theoretically analyzed. Then, the simulated experiments with several datasets are designed. Comparisons are made between the proposed method and the method based on the 5-folds cross validation. The results show that the proposed method is effective and takes less time, and it would be very suitable for target recognition problems.