.png){kind=icon}
.png){kind=icon}
Return
A multiple kernel framework for inductive semi-supervised SVM learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2011.12.036.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Multiple kernel learning
Inductive semi-supervised learning
Transductive SVM
DC programming
BCI application
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
No organization information available