Recursive finite Newton algorithm for support vector regression in the primal
Recursive finite Newton algorithm for support vector regression in the primal
复制标题
原始支持向量回归的递归有限牛顿算法
DOI：
10.1162/neco.2007.19.4.1082
复制
发表时间：
2007-04-01
影响因子：
2.9
通讯作者：
Jiao, Licheng
中科院分区：
文献类型：
--
作者：
Bo, Liefeng;Wang, Ling;Jiao, Licheng
最近提出了一些原始算法用于训练支持向量机。这封信遵循这些研究并开发了一种用于训练非线性支持向量回归的递归有限牛顿算法 (IHLF-SVR-RFN)。详细讨论了不敏感的 Huber 损失函数和牛顿步的计算。与 LIBSVM 2.82 的比较表明，所提出的算法给出了有希望的结果。
Some algorithms in the primal have been recently proposed for training support vector machines. This letter follows those studies and develops a recursive finite Newton algorithm (IHLF-SVR-RFN) for training nonlinear support vector regression. The insensitive Huber loss function and the computation of the Newton step are discussed in detail. Comparisons with LIBSVM 2.82 show that the proposed algorithm gives promising results.