An Improved Training Algorithm for the Linear Ranking Support Vector Machine

被引:0
|
作者
Airola, Antti [1 ]
Pahikkala, Tapio [1 ]
Salakoski, Tapio [1 ]
机构
[1] Univ Turku, Joukahaisenkatu 3-5 B, Turku, Finland
基金
芬兰科学院;
关键词
binary search tree; cutting plane optimization; learning to rank; support vector machine;
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
We introduce an O(ms + m log(m)) time complexity method for training the linear ranking support vector machine, where in is the number of training examples, and s the average number of non-zero features per example. The method generalizes the fastest previously known approach, which achieves the same efficiency only in restricted special cases. The excellent scalability of the proposed method is demonstrated experimentally.
引用
收藏
页码:134 / +
页数:2
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