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- W2385200041 abstract "Sphere structure SVMs classify multi-class data by their own hyperspheres,so the computational complexity of a quadratic programming problem is reduced obviously.Based on sphere structure SVMs,a new algorithm called sub-hypersphere SVMs was proposed to solve multi-class classification problems.It improved the method of training and judging in the overlap of the hyperspheres to improve classification precision.The definitions,overlap numbers and total overlap numbers and overlap ratio,were given which were relative to the overlap of the hyperspheres.According to the definitions above,the effect of the kernel parameterσ(radial basis function)was analyzed on the position of hyperspheres. Simulations were performed on two groups of real data.The results show that the new algorithm is effective and the analysis ofσis correct." @default.
- W2385200041 created "2016-06-24" @default.
- W2385200041 creator A5046024517 @default.
- W2385200041 date "2008-01-01" @default.
- W2385200041 modified "2023-09-25" @default.
- W2385200041 title "Improved Algorithm Based on Sphere Structure SVMs and Simulation" @default.
- W2385200041 hasPublicationYear "2008" @default.
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