《计算机应用研究》|Application Research of Computers

模糊多核支持向量机研究进展

Research progress of fuzzy multiple kernel support vector machine

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作者 戴小路,汪廷华,胡振威
机构 赣南师范大学 数学与计算机科学学院,江西 赣州 341000
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文章编号 1001-3695(2021)10-003-2896-08
DOI 10.19734/j.issn.1001-3695.2021.01.0035
摘要 模糊多核支持向量机将模糊支持向量机与多核学习方法结合,通过构造隶属度函数和利用多个核函数的组合形式有效缓解了传统支持向量机模型对噪声数据敏感和多源异构数据学习困难等问题,广泛应用于模式识别和人工智能领域。综述了模糊多核支持向量机的理论基础及其研究现状,详细介绍模糊多核支持向量机中的关键问题,即模糊隶属度函数设计与多核学习方法,最后对模糊多核支持向量机算法未来的研究进行展望。
关键词 核方法; 模糊支持向量机; 多核学习; 隶属度函数
基金项目 国家自然科学基金资助项目(61966002,62041210)
赣南师范大学研究生创新基金资助项目(YCX20A019)
本文URL http://www.arocmag.com/article/01-2021-10-003.html
英文标题 Research progress of fuzzy multiple kernel support vector machine
作者英文名 Dai Xiaolu, Wang Tinghua, Hu Zhenwei
机构英文名 School of Mathematics & Computer Science,Gannan Normal University,Ganzhou Jiangxi 341000,China
英文摘要 Fuzzy multiple kernel support vector machine(SVM) combines fuzzy SVM with multiple kernel learning(MKL) method which effectively reduces the sensitivity to noises and learning difficulty with the multi-source and heterogeneous data of the traditional SVM model by utilizing membership functions and combinations of multiple kernel functions. Fuzzy multiple kernel SVM has been widely applied in the pattern recognition and artificial intelligence community. This paper summarized the theoretical basis of fuzzy multiple kernel SVM and its current research status. Specifically, this paper were comprehensively reviewed the key problems of the fuzzy multiple kernel SVM, i. e., the design of fuzzy membership functions and MKL methods. Finally, this paper prospected the future research of fuzzy multiple kernel SVM.
英文关键词 kernel method; fuzzy support vector machine; multiple kernel learning; membership function
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收稿日期 2021/1/22
修回日期 2021/3/15
页码 2896-2903
中图分类号 TP181
文献标志码 A