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For the model, we first show that it is a good convex approximation to the basic chance-constrained optimization model for the problem, then with the help of Lagrange duality theory, we transform it into a deterministic semi-definite programming (SDP) which can be numerically solved by the state-of-the-art SDP solvers. Numerical experiments conducted on the artificial and the real benchmark datasets show the validity and the efficiency of the proposed model. <\/jats:p>","DOI":"10.1142\/s0217595924500313","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T06:31:30Z","timestamp":1730269890000},"source":"Crossref","is-referenced-by-count":0,"title":["A CVaR-Based Programming for Support Vector Machine with Uncertain Information"],"prefix":"10.1142","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8078-6055","authenticated-orcid":false,"given":"Jiakang","family":"Du","sequence":"first","affiliation":[{"name":"School of Management Science, Qufu Normal University, Rizhao Shandong 276800, P. R. 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