Sparse Learning for Linear Twin Parameter-margin Support Vector Machine
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Shuanghong Qu, Renato De Leone, and Min HuangAuthors Info & Claims
Shuanghong Qu, Renato De Leone, and Min HuangAuthors Info & ClaimsAbstract
Twin Parameter-margin support vector machine (TPMSVM) is a recent very powerful binary classifier. To improve its sparsity, a linear sparse TPMSVM (Lin-STPMSVM) is proposed in this paper. In the primal problem, the vectors defining the hyperplane are replaced with their expression in terms of the dual variables as derived from Karush Khun Tucker (KKT) conditions. Then the new primal problems are directly optimized, thus ensuring the sparsity of the solutions. Numerical experiments show that the solution obtained by new model is more sparse without reducing the accuracy. Therefore, Lin-STPMSVM not only inherits the advantages of TPMSVM, but also has the characteristics of sparsity, stability and robustness in dealing with classification problems.
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- Sparse Learning for Linear Twin Parameter-margin Support Vector Machine
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Published In
March 2024
478 pages
ISBN:9798400716416
DOI:10.1145/3654823
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Published: 29 May 2024
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- the European Union - Next Generation EU under the Italian Ministry of University and Research (MUR) National Innovation Ecosystem grant
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CACML 2024
CACML 2024: 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
March 22 - 24, 2024
Shanghai, China
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Overall Acceptance Rate 93 of 241 submissions, 39%
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