TY - GEN
T1 - Least square support vector machine for large scale dataset
AU - Nguyen, Khanh
AU - Le, Trung
AU - Lai, Vinh
AU - Nguyen, Duy
AU - TRAN, Dat
AU - MA, Wanli
PY - 2015
Y1 - 2015
N2 - Support Vector Machine (SVM) is a very well-known tool for classification and regression problems. Many applications require SVMs with non-linear kernels for accurate classification. Training time complexity for SVMs with non-linear kernels is typically quadratic in the size of the training dataset. In this paper, we depart from the very well-known variation of SVM, the so-called Least Square Support Vector Machine, and apply Steepest Sub-gradient Descent method to propose Steepest Sub-gradient Descent Least Square Support Vector Machine (SGD-LSSVM). It is theoretically proven that the convergent rate of the proposed method to gain ε - precision solution is O (log (1/ε)). The experiments established on the large-scale datasets indicate that the proposed method offers the comparable classification accuracies while being faster than the baselines
AB - Support Vector Machine (SVM) is a very well-known tool for classification and regression problems. Many applications require SVMs with non-linear kernels for accurate classification. Training time complexity for SVMs with non-linear kernels is typically quadratic in the size of the training dataset. In this paper, we depart from the very well-known variation of SVM, the so-called Least Square Support Vector Machine, and apply Steepest Sub-gradient Descent method to propose Steepest Sub-gradient Descent Least Square Support Vector Machine (SGD-LSSVM). It is theoretically proven that the convergent rate of the proposed method to gain ε - precision solution is O (log (1/ε)). The experiments established on the large-scale datasets indicate that the proposed method offers the comparable classification accuracies while being faster than the baselines
KW - Support Vector Machine
KW - kernel method
KW - solver
KW - steepest gradient descent
UR - http://www.scopus.com/inward/record.url?scp=84951031601&partnerID=8YFLogxK
U2 - 10.1109/ijcnn.2015.7280575
DO - 10.1109/ijcnn.2015.7280575
M3 - Conference contribution
SN - 9781479919611
VL - 1
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2057
EP - 2065
BT - 2015 International Joint Conference on Neural Networks, IJCNN 2015
A2 - Hussain, Amir
PB - IEEE
CY - USA
T2 - 2015 International Joint Conference on Neural Networks
Y2 - 12 July 2015 through 17 July 2015
ER -