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
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;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580731
