Binary coded output support vector machine

Authors:
Tao Ye;Xuefeng Zhu
Affiliations:
College of Automation Science and Engineering, South China University of Technology, Guangzhou, P.R. China;College of Automation Science and Engineering, South China University of Technology, Guangzhou, P.R. China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

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Abstract

To solve multi-class classification problems for large-scale datasets, the authors propose a coded output support vector machine (COSVM) by introducing the idea of information coding. The COSVM is built based on the support vector regression (SVR) machine that is implemented by the sequential minimal optimization (SMO) algorithm. The paper first introduces the soft ε-tube SVR's basic principles, next gives the idea and procedure of the SMO algorithm, and then illustrates the COSVM's topology. For studying the parameters impact on the binary COSVM's performance, we perform two experiments with the Character Trajectories dataset, in which output labels are coded with the binary number system. And some useful results are obtained in these experiments. The final section gives a conclusion and further research ideas.