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http://dx.doi.org/10.5351/KJAS.2005.18.2.421

Study on Support Vector Machines Using Mathematical Programming  

Yoon, Min (Yonsei Institute of Statistics Science)
Lee, Hak-Bae (Department of Applied Statistics)
Publication Information
The Korean Journal of Applied Statistics / v.18, no.2, 2005 , pp. 421-434 More about this Journal
Abstract
Machine learning has been extensively studied in recent years as effective tools in pattern classification problem. Although there have been several approaches to machine learning, we focus on the mathematical programming (in particular, multi-objective and goal programming; MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining much popularity recently. In pattern classification problem with two class sets, the idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. However, the idea of maximal margin separation is not quite new: in 1960's the multi-surface method (MSM) was suggested by Mangasarian. In 1980's, linear classifiers using goal programming were developed extensively. This paper proposes a new family of SVM using MOP/GP techniques, and discusses its effectiveness throughout several numerical experiments.
Keywords
Support vector machine; Maximal margin classifier; Multi-objective optimization; Goal programming;
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