TY - GEN
T1 - An SMO Approach to fast SVM for classification of large scale data
AU - Lin, Juanxi
AU - Song, Mengnan
AU - Hu, Jinglu
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/23
Y1 - 2014/1/23
N2 - In this paper, a novel approach is proposed as a new fast Support Vector Machines (SVM) basing on sequential minimal optimization(SMO), minimum enclosing ball(MEB) approach and active set strategy. The combination with these 3 techniques largely accelerates the training process of SVM, attains fewer support vectors(SVs) as well as obtains a acceptable accuracy comparing to original SVM. From simulation results, it is stated that the proposed method will be a good alternative for classification of large scale data.
AB - In this paper, a novel approach is proposed as a new fast Support Vector Machines (SVM) basing on sequential minimal optimization(SMO), minimum enclosing ball(MEB) approach and active set strategy. The combination with these 3 techniques largely accelerates the training process of SVM, attains fewer support vectors(SVs) as well as obtains a acceptable accuracy comparing to original SVM. From simulation results, it is stated that the proposed method will be a good alternative for classification of large scale data.
KW - Support Vector Machine
KW - active set
KW - minimum enclosing ball
KW - sequential minimal optimization
UR - http://www.scopus.com/inward/record.url?scp=84988259230&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988259230&partnerID=8YFLogxK
U2 - 10.1109/ICITCS.2014.7021735
DO - 10.1109/ICITCS.2014.7021735
M3 - Conference contribution
AN - SCOPUS:84988259230
T3 - 2014 International Conference on IT Convergence and Security, ICITCS 2014
BT - 2014 International Conference on IT Convergence and Security, ICITCS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th 2014 International Conference on IT Convergence and Security, ICITCS 2014
Y2 - 28 October 2014 through 30 October 2014
ER -