DocumentCode
2249176
Title
Canonical duality solution to support vector machine
Author
Yuan, Yubo ; Cao, Feilong
Author_Institution
Inst. of Metrol. & Comput. Sci., China Jiliang Univ., Hangzhou, China
Volume
6
fYear
2010
fDate
11-14 July 2010
Firstpage
3140
Lastpage
3145
Abstract
Support vector machine (SVM) is one of the most popular machine learning method and educed from a binary data classification problem. In this paper, a new duality theory named canonical duality theory is presented to solve the normal model of SVM. Several examples are illustrated to show that the exact solution can be obtained after the canonical duality problem being solved. Moreover, the support vectors can be located by non-zero elements of the canonical dual solution.
Keywords
duality (mathematics); learning (artificial intelligence); pattern classification; support vector machines; binary data classification problem; canonical duality solution; duality theory; machine learning; support vector machine; Bridges; BFGS method; classification; data mining; quadratic programming; smooth function; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location
Qingdao
Print_ISBN
978-1-4244-6526-2
Type
conf
DOI
10.1109/ICMLC.2010.5580731
Filename
5580731
Link To Document