Support vector selection and adaptation for classification of earthquake images

In this paper, we propose a new machine learning algorithm that we named Support Vector Selection and Adaptation (SVSA). Our aim is to achieve the classification performance of the nonlinear support vector machines (SVM) by using only the support vectors of the linear SVM. The proposed method does not require any type of kernels, and requires less computation time compared to the nonlinear SVM. The SVSA algorithm has two steps: selection and adaptation. In the first step, some of the support vectors obtained from linear SVM are selected. Then the selected support vectors are adapted iteratively in the training algorithm. The proposed method are compared against the linear and nonlinear SVM on synthetic and real remote sensing data. The results show that the proposed SVSA algorithm achieves very close performance to nonlinear SVM without any kernels in less computation time.