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A New Support Vector Machine and Its Learning Algorithm

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5 Author(s)
Haoran Zhang ; Coll. of Inf. Sci. & Eng., Zhejiang Normal Univ., Jinhua ; Changjiang Zhang ; Xiaodong Wang ; Xiuling Xu
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Support vector machine is a learning technique based on the structural risk minimization principle, this paper proposes a new kind of support vector machine (SVM), which modifies the classical SVM formulation to get even simpler dual optimization problem, then gives a quadratic optimization theorem, and according to it derives a multiplicative updates algorithm for solving the dual optimization problem. The updates algorithms converge monotonically to the solution of the optimal problem, and have a simple closed form. Experimental results of simulation indicate the feasibility of the varied regression support vector machine and its training algorithm

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Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on  (Volume:1 )

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