Training a support vector machine in the primal
Training a support vector machine in the primal
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DOI：
10.1162/neco.2007.19.5.1155
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发表时间：
2007-05-01
影响因子：
2.9
通讯作者：
Chapelle, Olivier
中科院分区：
文献类型：
--
作者：
Chapelle, Olivier
大多数支持向量机的文献都集中在对偶优化问题上。在这封信中，我们指出，原始问题也可以有效地解决线性和非线性支持向量机，没有理由忽略这种可能性。相反，从原始的角度来看，可以研究用于大规模SVM训练的新算法家族。
Most literature on support vector machines (SVMs) concentrates on the dual optimization problem. In this letter, we point out that the primal problem can also be solved efficiently for both linear and nonlinear SVMs and that there is no reason for ignoring this possibility. On the contrary, from the primal point of view, new families of algorithms for large-scale SVM training can be investigated.