10th EAI International Conference on Mobile Multimedia Communications

Research Article

Research Survey on Support Vector Machine

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  • @INPROCEEDINGS{10.4108/eai.13-7-2017.2270596,
        author={Huibing Wang and Jinbo Xiong and Zhiqiang Yao and Mingwei Lin and Jun Ren},
        title={Research Survey on Support Vector Machine},
        proceedings={10th EAI International Conference on Mobile Multimedia Communications},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2017},
        month={12},
        keywords={svm mobile multimedia optimization algorithms svm extension application},
        doi={10.4108/eai.13-7-2017.2270596}
    }
    
  • Huibing Wang
    Jinbo Xiong
    Zhiqiang Yao
    Mingwei Lin
    Jun Ren
    Year: 2017
    Research Survey on Support Vector Machine
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.13-7-2017.2270596
Huibing Wang1, Jinbo Xiong2, Zhiqiang Yao2,*, Mingwei Lin2, Jun Ren1
  • 1: Faculty of Software, Fujian Normal University
  • 2: Fujian Engineering Research Center of Public Service Big Data Mining and Application
*Contact email: zyy837603010g@163.com

Abstract

Support vector machine (SVM) as a learning machine has shown a good learning ability and generalization ability in classification, regression and forecasting. Because of its excellent learning performance, SVM has always been a hotspot in machine learning. In hence, this paper will make a more systematic introduction of SVM, including the theory of SVM, the summarization and comparison of the quadratic programming optimizations and parameter optimizations for SVM and the introduction of some new SVMs, like FSVM, TSVM, MSVM, etc. Then, the applications of SVM in real life will be presented, especially, in the area of mobile multimedia. Finally, we conclude with a discussion of the direction of further SVM improvement.