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《Journal of Zhejiang University(Engineering Science)》 2009-01
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Recursive training algorithm for one-class support vector machine based on active set method

XU Lei,ZHAO Guang-zhou,GU Hong(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China)  
A recursive algorithm based on active set method was proposed considering the sparsity of the solution of quadratic programming problem(QPP) to solve the QPP of one-class support vector machine(1-SVM).The global solution was obtained by the iteration of the local optimizations of active set with support vectors treated as the active set.And a recursive method was introduced to reduce the computational cost.The algorithm searched for the optimal distribution of support vectors unlike the sequential minimal optimization(SMO) algorithm focusing on the convergence of object function.Then the absolutely analytical solution was obtained without the sensitivity of Karush-Kuhn-Tucker(KKT) tolerance.The simulation results show that the algorithm outperforms SMO in time consumption and accuracy and can be efficiently applied to 1-SVM for large scale systems.
【Fund】: 国家自然科学基金资助项目(60702023);; 浙江省自然科学基金资助项目(Y107440)
【CateGory Index】: TP181
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