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Artificial Intelligence Review

, Volume 51, Issue 1, pp 19–32 | Cite as

Granular support vector machine: a review

  • Husheng Guo
  • Wenjian WangEmail author
Article
  • 313 Downloads

Abstract

The time complexity of traditional support vector machine (SVM) is \(O(l^{3})\) and l is the the training sample size, and it can not solve the large scale problems. Granular support vector machine (GSVM) is a novel machine learning model based on granular computing and statistical learning theory, and it can solve the low efficiency learning problem that exists in the traditional SVM and obtain satisfactory generalization performance, as well. This paper primarily reviews the past (rudiment), present (basic model) and future (development direction) of GSVM. Firstly, we briefly introduce the basic theory of SVM and GSVM. Secondly, we describe the past related research works conducted before the GSVM was proposed. Next, the latest thoughts, models, algorithms and applications of GSVM are described. Finally, we note the research and development prospects of GSVM.

Keywords

Granular support vector machine Support vector machine Rudiment Basic model Prospects 

Notes

Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments and suggestions. The work described in this paper was partially supported by the National Natural Science Foundation of China (Nos. 61503229, 61673249), Research Project Supported by Shanxi Scholarship Council of China (No. 2016-004), Natural Science Foundation of Shan Xi Province (No. 2015021096) and Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (No. 2015110).

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
  2. 2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of EducationShanxi UniversityTaiyuanChina

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