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dc.contributor.authorGu, Weizheen_US
dc.contributor.authorChen, Wei-Poen_US
dc.contributor.authorKo, Chun-Hsuen_US
dc.contributor.authorLee, Yuh-Jyeen_US
dc.contributor.authorChen, Jein-Shanen_US
dc.date.accessioned2018-08-21T05:53:29Z-
dc.date.available2018-08-21T05:53:29Z-
dc.date.issued2018-05-01en_US
dc.identifier.issn0926-6003en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s10589-017-9975-9en_US
dc.identifier.urihttp://hdl.handle.net/11536/144747-
dc.description.abstractIn this paper, we propose two new smooth support vector machines for -insensitive regression. According to these two smooth support vector machines, we construct two systems of smooth equations based on two novel families of smoothing functions, from which we seek the solution to -support vector regression (-SVR). More specifically, using the proposed smoothing functions, we employ the smoothing Newton method to solve the systems of smooth equations. The algorithm is shown to be globally and quadratically convergent without any additional conditions. Numerical comparisons among different values of parameter are also reported.en_US
dc.language.isoen_USen_US
dc.subjectSupport vector machineen_US
dc.subjecte-insensitive loss functionen_US
dc.subjecte-smooth support vector regressionen_US
dc.subjectSmoothing Newton algorithmen_US
dc.titleTwo smooth support vector machines for epsilon-insensitive regressionen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s10589-017-9975-9en_US
dc.identifier.journalCOMPUTATIONAL OPTIMIZATION AND APPLICATIONSen_US
dc.citation.volume70en_US
dc.citation.spage171en_US
dc.citation.epage199en_US
dc.contributor.department應用數學系zh_TW
dc.contributor.departmentDepartment of Applied Mathematicsen_US
dc.identifier.wosnumberWOS:000428611200006en_US
Appears in Collections:Articles