- Title
- An efficient classification using support vector machines
- Creator
- Ruan, Ning; Chen, Yi; Gao, David
- Date
- 2013
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/41255
- Identifier
- vital:5756
- Identifier
- ISBN:9780989319300 (ISBN)
- Abstract
- Support vector machine (SVM) is a popular method for classification in data mining. The canonical duality theory provides a unified analytic solution to a wide range of discrete and continuous problems in global optimization. This paper presents a canonical duality approach for solving support vector machine problem. It is shown that by the canonical duality, these nonconvex and integer optimization problems are equivalent to a unified concave maximization problem over a convex set and hence can be solved efficiently by existing optimization techniques. © 2013 The Science and Information Organization.
- Publisher
- London; United Kingdom
- Relation
- Proceedings of 2013 Science and Information Conference, SAI 2013 p. 585-589
- Rights
- This metadata is freely available under a CCO license
- Subject
- Canonical duality; Classification; Data mining; Global optimization; Support vector machine; Analytic solution; Canonical duality theories; Continuous problems; Convex set; Integer optimization; Maximization problem; Optimization techniques; Classification (of information); Integer programming; Optimization; Set theory; Support vector machines
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