Working Set Selection Using Second Order Information for Training Support Vector Machines
Resource
Journal of Machine Learning Research 6,1889-1918
Journal
Journal of Machine Learning Research
Pages
1889-1918
Date Issued
2005
Date
2005
Author(s)
DOI
20060927122855804710
Abstract
Working set selection is an important step in decomposition methods for training support
vector machines (SVMs). This paper develops a new technique for working set selection in
SMO-type decomposition methods. It uses second order information to achieve fast con-
vergence. Theoretical properties such as linear convergence are established. Experiments
demonstrate that the proposed method is faster than existing selection methods using first
order information.
Subjects
support vector machines
decomposition methods
sequential minimal optimization
working set selection
Publisher
臺北市:國立臺灣大學資訊工程學系
Type
journal article
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