Miao YU, Adel RHUMA, Syed Mohsen NAQVI, Jonathon CHAMBERS
(Advanced Signal Processing Group, Electronic and Electrical Engineering Department, Loughborough University, Loughborough, Leics LE11 3TU, UK)
Abstract-In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine (DAGSVM) for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structure of the directed acyclic graph is not chosen random and fixed, and it can be adaptive to be optimal according to the incoming testing samples, thus it has a good generalization performance. From experiments on six datasets, we can see that the proposed improved version of DAGSVM is better than the traditional one with respect to the accuracy rate.
Key words-class classification; directed acyclic graph; support vector machine
Manuscript Number: 1674-8042(2011)04-0367-04
doi: 10.3969/j.issn.1674-8042.2011.04.015
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