A multiple kernel framework for inductive semi-supervised SVM learning

详细信息	查看全文 | 推荐本文 |

• 作者：Xilan Tian ; ^{xilan.tian@insa-rouen.fr} ; ^{xilan.tian@gmail.com}  ; [Author Vitae] ; Gilles Gasso ^{gilles.gasso@insa-rouen.fr}  ; [Author Vitae] ; Sté ; phane Canu ^{stephane.canu@insa-rouen.fr}  ; [Author Vitae]
• 关键词：Multiple kernel learning ; Inductive semi-supervised learning ; Transductive SVM ; DC programming ; BCI application
• 刊名：Neurocomputing
• 出版年：2012
• 期刊代码：95_09252312
• 类别：cp
• 出版时间：1 August, 2012
• 卷：90
• 期：Complete
• 页码：46-58
• 文件大小：607 K

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.