TY - GEN
T1 - Support vector selection and adaptation for classification of earthquake images
AU - Kaya, G. Taşkin
AU - Ersoy, O. K.
AU - Kamaşak, M. E.
PY - 2009
Y1 - 2009
N2 - 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 traning algorithm. The proposed method are compared against the linear and nonlinear SVM on sythetic 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.
AB - 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 traning algorithm. The proposed method are compared against the linear and nonlinear SVM on sythetic 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.
KW - Classification of earthquake images
KW - Support vector machines
KW - Support vector selection and adaptation
UR - https://www.scopus.com/pages/publications/77951126745
U2 - 10.1109/IGARSS.2009.5418229
DO - 10.1109/IGARSS.2009.5418229
M3 - Conference contribution
AN - SCOPUS:77951126745
SN - 9781424433957
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - II851-II854
BT - 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
T2 - 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
Y2 - 12 July 2009 through 17 July 2009
ER -