支持向量机研究进展

计算机科学 ›› 2011, Vol. 38 ›› Issue (2): 14-17.

支持向量机研究进展

顾亚祥，丁世飞

(中国矿业大学计算机科学与技术学院徐州221116)(中国科学院计算技术研究所智能信息处理重点实验室北京100080)

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受江苏省自然科学基金项目(BK2009093)，国家自然科学基金项目(60975039)资助。

Advances of Support Vector Machines(SVM)

GU Ya-xiang,DING Shi-fei

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 基于统计学习理论的支持向量机((Support vector machines, SVM)以其优秀的学习能力受到广泛的关注。但传统支持向量机在处理大规模二次规划问题时会出现训练时间长、效率低下等问题。对SVM训练算法的最新研究成果进行了综述，对主要算法进行了比较深入的分析和比较，指出了各自的优点及其存在的问题，并且着重介绍了目前研究的新进展—模糊SVM和粒度SVM。接着论述了SVM主要的两方面应用—分类和回归。最后给出了今后SVM研究方向的预见。

关键词: 支持向量机，训练算法，模糊支持向量机，粒度支持向量机

Abstract: Support vector machines(SVM) arc widespread attended for its excellent ability to learn, that arc based on statistical learning theory. But in dealing with large-scale quadratic programming ( QP) problem, traditional SVM will take too long time of training time, and has low efficiency and so on. This paper made a summarize of the new progress in the SVM training of algorithm, and made analysis and comparison on main algorithm, pointed out the advantages and disadvantages of them,focused on new progress in the current study-Fuzzy Support Vector Machine and Granular Support Vector Machine. Then the two mainly applications-classification and regression of SVM were discussed. Finally, the article gave the future research directions on SVM prediction.

Key words: Support vector machine, Training algorithm, Fuzzy SVM, Granular SVM

顾亚祥，丁世飞. 支持向量机研究进展[J]. 计算机科学, 2011, 38(2): 14-17. https://doi.org/

GU Ya-xiang,DING Shi-fei. Advances of Support Vector Machines(SVM)[J]. Computer Science, 2011, 38(2): 14-17. https://doi.org/

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