DocumentCode
442109
Title
A hybrid decomposition/interior point algorithm for massive support vector machine
Author
Li, Jin ; Liu, Jing-Xu ; Tan, Yue-jin ; Liao, Liang-Cai
Author_Institution
Dept. of Manage., Nat. Univ. of Defense Technol., Changsha, China
Volume
7
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
4281
Abstract
A hybrid decomposition/interior point algorithm for training SVM has been proposed. The hybrid method applies an interior point method to the quadratic programs arising from decomposition algorithm, and then some techniques are presented to improve the performance of algorithm. It has been demonstrated that the algorithm is applicable for training SVM, especially effective for massive SVM.
Keywords
learning (artificial intelligence); quadratic programming; support vector machines; SVM training; hybrid decomposition-interior point algorithm; quadratic program; support vector machine; Cybernetics; Equations; Large-scale systems; Machine learning; Management training; Neural networks; Quadratic programming; Support vector machine classification; Support vector machines; Technology management; Decomposition; Hybrid; Interior Point; Support Vector Machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527689
Filename
1527689
Link To Document