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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Linear Multi-class Classification Support Vector Machine
verfasst von : Yan Xu, Yuanhai Shao, Yingjie Tian, Naiyang Deng
Erschienen in: Cutting-Edge Research Topics on Multiple Criteria Decision Making
Verlag: Springer Berlin Heidelberg
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Support Vector Machines (SVMs) for classification have been shown to be promising classification tools in many real-world problems. How to effectively extend binary SVC to multi-class classification is still an on-going research issue. In this article, instead of solving quadratic programming (QP) in algorithm in [1], utilizing a linear function in the objective function a linear programming (LP) problem is introduced in our algorithm,thus leading to a new algorithm for multi-class problem named linear multi-class classification support vector machine. Numerical experiments on artificial data sets and benchmark data sets show that the proposed method is comparable to algorithm [1] in errors, while considerably ten times faster and the same robustness.