{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:43:29Z","timestamp":1777704209594,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2020,8,6]],"date-time":"2020-08-06T00:00:00Z","timestamp":1596672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,10,7]]},"abstract":"<jats:p>\n                    The support vector machine is a classification approach in machine learning. The second-order cone optimization formulation for the soft-margin support vector machine can ensure that the misclassification rate of data points do not exceed a given value. In this paper, a novel second-order cone programming formulation is proposed for the soft-margin support vector machine. The novel formulation uses the\n                    <jats:italic>l<\/jats:italic>\n                    <jats:sub>2<\/jats:sub>\n                    -norm and two margin variables associated with each class to maximize the margin. Two regularization parameters\n                    <jats:italic>\u03b1<\/jats:italic>\n                    and\n                    <jats:italic>\u03b2<\/jats:italic>\n                    are introduced to control the trade-off between the maximization of margin variables. Numerical results illustrate that the proposed second-order cone programming formulation for the soft-margin support vector machine has a better prediction performance and robustness than other second-order cone programming support vector machine models used in this article for comparision.\n                  <\/jats:p>","DOI":"10.3233\/jifs-200467","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T15:54:36Z","timestamp":1597161276000},"page":"4505-4513","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel second-order cone programming support vector machine model for binary data classification"],"prefix":"10.1177","volume":"39","author":[{"given":"Guishan","family":"Dong","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xidian University, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuewen","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xidian University, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,8,6]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","unstructured":"KhemchandaniR. and JayadevaS.C. 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