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Therefore, compared with the standard support vector regression, our approach is much efficient due to kernel-free and solving a set of linear equations. Numerical results illustrate that our approach has better performance than other existing regression approaches in terms of regression criterion and CPU time.<\/jats:p>","DOI":"10.3233\/ida-205094","type":"journal-article","created":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T12:47:45Z","timestamp":1615294065000},"page":"265-281","source":"Crossref","is-referenced-by-count":19,"title":["Quadratic hyper-surface kernel-free least squares support vector regression"],"prefix":"10.1177","volume":"25","author":[{"given":"Junyou","family":"Ye","sequence":"first","affiliation":[{"name":"College of Mathematics and Systems Science, Xinjiang University, Urumuqi, China"}]},{"given":"Zhixia","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Systems Science, Xinjiang University, Urumuqi, China"},{"name":"Institute of Mathematics and Physics, Xinjiang University, Urumqi, China"}]},{"given":"Zhilin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Mathematics, North Carolina State University, Raleigh, NC, USA"}]}],"member":"179","reference":[{"issue":"6","key":"10.3233\/IDA-205094_ref1","doi-asserted-by":"crossref","first-page":"2110","DOI":"10.1016\/j.patcog.2015.01.009","article-title":"Fuzzy support vector machines for multilabel classification","volume":"48","author":"Abe","year":"2015","journal-title":"Pattern Recognition"},{"issue":"4","key":"10.3233\/IDA-205094_ref3","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1007\/s10878-015-9848-z","article-title":"Quadratic kernel-free least squares support vector machine for target diseases classification","volume":"30","author":"Bai","year":"2015","journal-title":"Journal of Combinatorial Optimization"},{"key":"10.3233\/IDA-205094_ref4","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.neunet.2013.12.003","article-title":"Lagrangian support vector regression via unconstrained convex minimization","volume":"51","author":"Balasundaram","year":"2014","journal-title":"Neural Netw."},{"key":"10.3233\/IDA-205094_ref5","doi-asserted-by":"crossref","unstructured":"B.E. 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