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直接支持向量回归机 被引量：1

Direct support vector machine for regression
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摘要 将超平面偏置项平方加入到最小二乘支持向量回归机(LSSVMR)的目标函数中,提出直接支持向量回归机(DSVMR)。该方法增强了求解问题的凸性,与LSSVMR相比,只需要求解一个与核矩阵类似的对称正定矩阵的逆就可以得到问题的解,再使用Cholesky分解和SMW(Sherman-Morrison-Woodbury)求逆公式,降低了计算复杂度,加快了学习速度,而且逼近能力与LSSVMR近乎相同。最后数值试验表明DSVMR可行且完全具有上述优势。 A direct support vector machine for regression （DSVMR） is obtained when appending the square bias of the hyper-plane into the object function in a least square support vector machine for regression （LSSVMR）. Compared with LSSVMR, the DSVMR strengthens the convexity of the problem to be solved and only needs to calculate the inversion of a symmetrical positive definite matrix which is similar to a kernel matrix. The ChoIesky decomposing and the SMW（Sherman- Morrison-Woodbury） formula are employed to reduce the computing amount greatly, accelerates the learning speed and has no weakening approximate ability. Numerical experiments show that the new method is feasible and has the above advantages.
作者 杜喆 刘三阳 吴青
机构地区 西安电子科技大学数学科学系
出处 《系统工程与电子技术》 EI CSCD 北大核心 2009年第1期178-181,共4页 Systems Engineering and Electronics
基金 国家自然科学基金资助课题(60574075 60705004)
关键词 支持向量机 直接支持向量机 回归 正定矩阵 support vector maehine direct support vector machine regression positive definite matrix
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献14

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共引文献25

同被引文献12

  • 1陈爱军,宋执环,李平.基于矢量基学习的最小二乘支持向量机建模[J].控制理论与应用,2007,24(1):1-5. 被引量：21
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系统工程与电子技术
2009年 第1期

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