Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 2
435-439

SVM classification algorithm based on ML loss

Xu Longfei
Yu Jinming
College of Information Science & Technology, Donghua University, Shanghai 201620, China
DOI: 10.19734/j.issn.1001-3695.2019.12.0666

Abstract

The loss function of SVM is able to guarantee the high confidence of classification results, but it is also an unbounded convex function which is greatly affected by noise. In order to improve the classification effect of SVM in noisy environment, this paper proposed ML loss combined with pinball and LS loss functions to reduce the sensitivity to noise, which was applied to SVM to obtain MLSVM model. The algorithm simplified the solution process according to the characteristics of LS loss function with structural risk minimization and equality constraints, then used pinball loss function to determine the classification hyperplanes according to the max quantile distance between classification samples and used Lagrange function and other methods to work out the objective function and classification hyperplanes of MLSVM. Experiments on datasets show that compared with hinge SVM and other models, MLSVM is capable of reducing the sensitivity to noise in data and improving the re-cognition performance of noise-containing data.

Foundation Support

国家自然科学基金资助项目(16K10439)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.12.0666
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 2
Section: Algorithm Research & Explore
Pages: 435-439
Serial Number: 1001-3695(2021)02-020-0435-05

Publish History

[2021-02-05] Printed Article

Cite This Article

徐龙飞, 郁进明. 基于ML loss的SVM分类算法 [J]. 计算机应用研究, 2021, 38 (2): 435-439. (Xu Longfei, Yu Jinming. SVM classification algorithm based on ML loss [J]. Application Research of Computers, 2021, 38 (2): 435-439. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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