Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives

被引：27
|
作者：
Jayadeva [2 ]
Khemchandani, Reshma [1 ]
Chandra, Suresh [1 ]
机构：
[1] Indian Inst Technol, Dept Math, New Delhi 110016, India
[2] Inst Area Vasant Kunj, IBM India Res Lab, New Delhi 110070, India
来源：
INFORMATION SCIENCES | 2008年 / 178卷 / 17期
关键词：
support vector machines; regularized least squares; machine learning; function approximation;
D O I：
10.1016/j.ins.2008.04.007
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
in this paper, we propose a regularized least squares approach based support vector machine for simultaneously approximating a function and its derivatives. The proposed algorithm is simple and fast as no quadratic programming solver needs to be employed. Effectively, only the solution of a structured system of linear equations is needed. (c) 2008 Published by Elsevier Inc.
引用
收藏
页码：3402 / 3414
页数：13
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