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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3092177
|2 doi
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|a pubmed24n1090.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Wang, Huajun
|e verfasserin
|4 aut
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|a Support Vector Machine Classifier via L0/1 Soft-Margin Loss
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 16.09.2022
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|a Date Revised 22.12.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Support vector machines (SVM) have drawn wide attention for the last two decades due to its extensive applications, so a vast body of work has developed optimization algorithms to solve SVM with various soft-margin losses. To distinguish all, in this paper, we aim at solving an ideal soft-margin loss SVM: L0/1 soft-margin loss SVM (dubbed as L0/1-SVM). Many of the existing (non)convex soft-margin losses can be viewed as one of the surrogates of the L0/1 soft-margin loss. Despite its discrete nature, we manage to establish the optimality theory for the L0/1-SVM including the existence of the optimal solutions, the relationship between them and P-stationary points. These not only enable us to deliver a rigorous definition of L0/1 support vectors but also allow us to define a working set. Integrating such a working set, a fast alternating direction method of multipliers is then proposed with its limit point being a locally optimal solution to the L0/1-SVM. Finally, numerical experiments demonstrate that our proposed method outperforms some leading classification solvers from SVM communities, in terms of faster computational speed and a fewer number of support vectors. The bigger the data size is, the more evident its advantage appears
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Shao, Yuanhai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Shenglong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Ce
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xiu, Naihua
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 10 vom: 24. Okt., Seite 7253-7265
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:44
|g year:2022
|g number:10
|g day:24
|g month:10
|g pages:7253-7265
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|u http://dx.doi.org/10.1109/TPAMI.2021.3092177
|3 Volltext
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