A multiple kernel framework for inductive semi-supervised SVM learning

被引：41

作者：

Tian, Xilan ^{[1
]}

Gasso, Gilles ^{[1
]}

Canu, Stephane ^{[1
]}

机构：

[1] INSA Rouen, LITIS EA 4108, F-76801 St Etienne, France

来源：

NEUROCOMPUTING | 2012年 / 90卷

关键词：

Multiple kernel learning; Inductive semi-supervised learning; Transductive SVM; DC programming; BCI application; ALGORITHM;

D O I：

10.1016/j.neucom.2011.12.036

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We investigate the benefit of combining both cluster assumption and manifold assumption underlying most of the semi-supervised algorithms using the flexibility and the efficiency of multiple kernel learning. The multiple kernel version of Transductive SVM (a cluster assumption based approach) is proposed and it is solved based on DC (Difference of Convex functions) programming. Promising results on benchmark data sets and the BCI data analysis suggest and support the effectiveness of proposed work. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：46 / 58

页数：13

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