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Artificial Intelligence Review

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19-04-2017

Granular support vector machine: a review

Authors: Husheng Guo, Wenjian Wang

Published in: Artificial Intelligence Review | Issue 1/2019

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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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Title: Granular support vector machine: a review
Authors: Husheng Guo
Wenjian Wang
Publication date: 19-04-2017
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 1/2019
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-017-9555-5

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