Fusing binary support vector machines (SVM) into multiclass SVM

Zilu Ying; Jingwen Li; Youwei Zhang

doi:10.1117/12.716969

24 October 2006 Fusing binary support vector machines (SVM) into multiclass SVM

Zilu Ying, Jingwen Li, Youwei Zhang

Proceedings Volume 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence; 63571B (2006) https://doi.org/10.1117/12.716969
Event: Sixth International Symposium on Instrumentation and Control Technology, 2006, Beijing, China

Abstract

Multi-class support vector machine by fusing a class of binary support vector machines is proposed. The classifier fusion approaches include simple combination method such as Maximum, Minimum, Product, Mean, Median and Major Voting. Dempster-Shafer fusion method is also presented as well as KNN and Neural network approaches. The proposed algorithms are applied to the facial expression recognition applications for both the Japanese female facial expression database and the Cohn-Kanade AU-coded facial expression database. The results show that it is effective to combine binary support vector machines (SVM) to a multi-class SVM.

Citation Download Citation

Zilu Ying, Jingwen Li, and Youwei Zhang "Fusing binary support vector machines (SVM) into multiclass SVM", Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 63571B (24 October 2006); https://doi.org/10.1117/12.716969

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