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.
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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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Guo, H., Wang, W. Granular support vector machine: a review. Artif Intell Rev 51, 19–32 (2019). https://doi.org/10.1007/s10462-017-9555-5
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DOI: https://doi.org/10.1007/s10462-017-9555-5