TY - GEN
T1 - A projection neural network for training support vector machines
AU - Liu, Fengqiu
AU - Zhang, Hongxu
AU - Qin, Sitian
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/3
Y1 - 2017/1/3
N2 - In this paper, we develop a projection neural network to solve the convex quadratic programming problem in support vector machine (SVM) learning. Then, we obtain a unique global solution for the proposed neural network. Furthermore, we prove that this network is completely stable and finite-time convergence. To present the feasibility and efficiency of the proposed neural network for solving the SVM learning problem, we provide several illustrative examples at the end.
AB - In this paper, we develop a projection neural network to solve the convex quadratic programming problem in support vector machine (SVM) learning. Then, we obtain a unique global solution for the proposed neural network. Furthermore, we prove that this network is completely stable and finite-time convergence. To present the feasibility and efficiency of the proposed neural network for solving the SVM learning problem, we provide several illustrative examples at the end.
KW - Finite-time convergence
KW - complete convergence
KW - neural network
KW - support vector machine
UR - https://www.scopus.com/pages/publications/85011022822
U2 - 10.1109/YAC.2016.7804907
DO - 10.1109/YAC.2016.7804907
M3 - 会议稿件
AN - SCOPUS:85011022822
T3 - Proceedings - 2016 31st Youth Academic Annual Conference of Chinese Association of Automation, YAC 2016
SP - 298
EP - 302
BT - Proceedings - 2016 31st Youth Academic Annual Conference of Chinese Association of Automation, YAC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st Youth Academic Annual Conference of Chinese Association of Automation, YAC 2016
Y2 - 11 November 2016 through 13 November 2016
ER -