A Dynamic Recognition Method Study Using the Support Vector Machine

Abstract

A dynamic recognition method using support vector machine (SVM) is researched in the paper, which is made up of the SVM based on target feature and the k-nearest neighbors. The SVM based on target feature is put forward by integrating the target feature with SVM, whose role is recognition of the target feature. It searches the optimal separating hyperplane of the local space taking the target feature as center, but does not search the optimal separating hyperplane of the whole space. To show better importance of each sample to the target feature, a method is put forward that the penalty function C i is measured. And the dynamic training set is reconstructed according to the penalty function C i and can be controlled dynamically by user, so the training time of the SVM based on target feature can controlled dynamically in a short time; therefore, the new target samples being obtained in the battlefield can applied in the SVM at once. At last, the dynamic recognition method is applied to the underwater target recognition that is utmost important to submarine war. Experiment results show that the dynamic recognition method using SVM is more robust than the traditional SVM.

Target recognition study using SVM, ANNs and expert knowledge

“…The SVM is applied widely more and more in many domain of AI, whose theoretical foundation is the statistical learning theory developed by Vapnik [7][8][9][10][11][12][13]. The formulation of SVM embodies the structural risk minimization (SRM) principle.…”