A new alpha seeding method for support vector machine training

被引：0
|
作者：
Feng, D [1 ]
Shi, WK [1 ]
Guo, HW [1 ]
Chen, LZ [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Sch Elect & Elect Engn, Shanghai 200030, Peoples R China
来源：
ADVANCES IN NATURAL COMPUTATION, PT 1, PROCEEDINGS | 2005年 / 3610卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In order to get good hyperparameters of SVM, user needs to conduct extensive cross-validation such as leave-one-out (LOO) cross-validation. Alpha seeding is often used to reduce the cost of SVM training. Compared with the existing schemes of alpha seeding, a new efficient alpha seeding method is proposed. Through some examples, its good performance has been proved. Interpretation from both geometrical and mathematical view is also given.
引用
收藏
页码：679 / 682
页数：4
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