{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:24:39Z","timestamp":1786980279507,"version":"build-2736575974"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,3,2]],"date-time":"2016-03-02T00:00:00Z","timestamp":1456876800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (CN)","doi-asserted-by":"publisher","award":["11401485"],"award-info":[{"award-number":["11401485"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2017,1]]},"DOI":"10.1007\/s00500-016-2089-y","type":"journal-article","created":{"date-parts":[[2016,3,2]],"date-time":"2016-03-02T03:29:50Z","timestamp":1456889390000},"page":"245-254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A new branch-and-bound approach to semi-supervised support vector machine"],"prefix":"10.1007","volume":"21","author":[{"given":"Ye","family":"Tian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2016,3,2]]},"reference":[{"issue":"12","key":"2089_CR1","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TNN.2009.2031143","volume":"20","author":"M Adankon","year":"2009","unstructured":"Adankon M, Cheriet M, Biem A (2009) Semisupervised least squares support vector machine. IEEE Trans Neural Netw 20(12):1858\u20131870","journal-title":"IEEE Trans Neural Netw"},{"key":"2089_CR2","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository. School of Information and Computer Science, University of California. http:\/\/archive.ics.uci.edu\/ml . Accessed 2 Aug 2015"},{"key":"2089_CR3","doi-asserted-by":"publisher","unstructured":"Bai Y, Yan X (2015) Conic relaxation for semi-supervised support vector machines. J Optim Theory Appl. doi: 10.1007\/s10957-015-0843-4","DOI":"10.1007\/s10957-015-0843-4"},{"issue":"1","key":"2089_CR4","first-page":"3","volume":"8","author":"Y Bai","year":"2012","unstructured":"Bai Y, Chen Y, Niu B (2012) SDP relaxation for semi-supervised support vector machine. Pac J Optim 8(1):3\u201314","journal-title":"Pac J Optim"},{"issue":"4","key":"2089_CR5","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1080\/02331934.2011.611515","volume":"62","author":"Y Bai","year":"2013","unstructured":"Bai Y, Niu B, Chen Y (2013) New SDP models for protein homology detection with semi-supervised SVM. Optimization 62(4):561\u2013572","journal-title":"Optimization"},{"key":"2089_CR6","first-page":"368","volume":"11","author":"K Bennett","year":"1999","unstructured":"Bennett K, Demiriz A (1999) Semi-supervised support vector machines. Adv Neural Inf Process Syst 11:368\u2013374","journal-title":"Adv Neural Inf Process Syst"},{"key":"2089_CR7","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10107-005-0637-9","volume":"109","author":"A Billionnet","year":"2007","unstructured":"Billionnet A, Elloumi S (2007) Using a mixed integer quadratic programming solver for the unconstrained quadratic 0\u20131 problem. Math Program 109:55\u201368","journal-title":"Math Program"},{"key":"2089_CR8","unstructured":"Blum A, Chawla S (2001) Learning from labeled and unlabeled data using graph mincuts. In: Proc. 18th int. conf. on mach. learn"},{"key":"2089_CR9","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/9780262033589.001.0001","volume-title":"Semi-supervised learning","author":"O Chapelle","year":"2006","unstructured":"Chapelle O, Sch\u00f6lkopf B, Zien A (2006) Semi-supervised learning. MIT press, Cambridge"},{"key":"2089_CR10","first-page":"203","volume":"9","author":"O Chapelle","year":"2008","unstructured":"Chapelle O, Sindhwani V, Keerthi S (2008) Optimization techniques for semi-supervised support vector machines. J Mach Learn Res 9:203\u2013233","journal-title":"J Mach Learn Res"},{"key":"2089_CR11","first-page":"1687","volume":"7","author":"R Collobert","year":"2006","unstructured":"Collobert R, Sinz F, Weston F, Bottou L (2006) Large scale transductive SVMs. J Math Learn Res 7:1687\u20131712","journal-title":"J Math Learn Res"},{"issue":"3","key":"2089_CR12","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"key":"2089_CR13","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511801389","volume-title":"An introduction to support vector machines and other kernel-based learning methods","author":"N Cristianini","year":"2000","unstructured":"Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge"},{"key":"2089_CR14","volume-title":"Advances in neural information processing systems","author":"T Bie de","year":"2004","unstructured":"de Bie T, Cristianini N (2004) Convex methods for transuction. In: Thrun S, Saul L, Sch\u00f6ikopf B (eds) Advances in neural information processing systems, vol 16. MIT Press, Cambridge"},{"key":"2089_CR15","doi-asserted-by":"crossref","unstructured":"de Bie T, Crisianini N (2006) Semi-supervised learning using semi-definite programming. In: Chapelle O, Sch\u00f6ikopf B, Zien A (eds) Semi-supervised learning. MIT Press, Cambridge","DOI":"10.7551\/mitpress\/6173.003.0011"},{"key":"2089_CR16","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neucom.2012.12.056","volume":"123","author":"F Gieseke","year":"2014","unstructured":"Gieseke F, Airola A, Pahikkala T, Kramer O (2014) Fast and simple gradient-based optimization for semi-supervised support vector machines. Neurocomputing 123:23\u201332","journal-title":"Neurocomputing"},{"key":"2089_CR17","unstructured":"Grant M, Boyd S (2010) CVX: matlab software for disciplined programming, version 1.2. http:\/\/cvx.com\/cvx . Accessed 15 Aug 2015"},{"key":"2089_CR18","doi-asserted-by":"crossref","unstructured":"Ha M, Wang C, Chen J (2013a) The support vector machine based on intuitionistic fuzzy number and kernel function. Soft Comput 4:635\u2013641","DOI":"10.1007\/s00500-012-0937-y"},{"key":"2089_CR19","doi-asserted-by":"crossref","unstructured":"Ha M, Yang Y, Wang C (2013b) A new support vector machine based on type-2 fuzzy samples. Soft Comput 11:2065\u20132074","DOI":"10.1007\/s00500-013-1122-7"},{"key":"2089_CR20","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1007\/s00500-010-0577-z","volume":"15","author":"Q He","year":"2011","unstructured":"He Q, Wu C (2011) Membership evaluation and feature selection for fuzzy support vector machine based on fuzzy rough sets. Soft Comput 15:1105\u20131114","journal-title":"Soft Comput"},{"key":"2089_CR21","first-page":"87C169","volume":"40","author":"R Horn","year":"1990","unstructured":"Horn R (1990) The hadamard product. Proc Symp Appl Math Am Math Soc 40:87C169","journal-title":"Proc Symp Appl Math Am Math Soc"},{"key":"2089_CR22","unstructured":"Joachims T (1999) Transductive inference for text classification using support vector machines. In: Int. conf. mach. learn., pp 200\u2013209"},{"key":"2089_CR23","first-page":"27","volume":"5","author":"G Lanckriet","year":"2004","unstructured":"Lanckriet G, Cristianini N, Bartlett P, Ghaoui L, Jordan M (2004) Learning the kernel matrix with semidefinite programming. J Mach Learn Res 5:27\u201372","journal-title":"J Mach Learn Res"},{"key":"2089_CR24","unstructured":"Lee C, Wang S, Jiao F, Schuurmans D, Greiner R (2006) Learning to model spatial dependency: semi-supervised discriminative random fields. In: Adv Neural Info Process Sys, vol 19"},{"key":"2089_CR25","unstructured":"Lu C, Guo X (2014) Convex reformulation for binary quadratic programming problems via average objective value maximization. Optim Lett. http:\/\/link.springer.com\/article\/10.1007\/s11590-014-0768-0 . Accessed 12 July 2015"},{"key":"2089_CR26","doi-asserted-by":"crossref","first-page":"357","DOI":"10.3934\/jimo.2016.12.357","volume":"12","author":"J Luo","year":"2016","unstructured":"Luo J, Fang S, Bai Y, Deng Z (2016) Fuzzy quadratic surface support vector machine based on fisher discriminant analysis. J Ind Manag Optim 12:357\u2013373","journal-title":"J Ind Manag Optim"},{"key":"2089_CR27","doi-asserted-by":"crossref","unstructured":"Osuna F, Freund R, Girosit F (1997) Training support vector machines: an application to face detection. In: Proc. comput. vis. pattern recognit., pp 130\u2013136","DOI":"10.1109\/CVPR.1997.609310"},{"key":"2089_CR28","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/BF00120662","volume":"1","author":"P Pardalos","year":"1991","unstructured":"Pardalos P, Vavasis S (1991) Quadratic programming with one negative eigenvalue is NP-Hard. J Glob Optim 1:15\u201322","journal-title":"J Glob Optim"},{"key":"2089_CR29","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1016\/j.patcog.2010.09.002","volume":"44","author":"S Reddy","year":"2011","unstructured":"Reddy S, Schevade S, Murty M (2011) A fast quasi-Newton method for semi-supervised SVM. Pattern Recognit 44:2305\u20132313","journal-title":"Pattern Recognit"},{"key":"2089_CR30","volume-title":"Learning with kernels: support vector machines, regularization, optimization and beyond","author":"B Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf B, Smola A (2002) Learning with kernels: support vector machines, regularization, optimization and beyond. MIT Press, Cambridge"},{"key":"2089_CR31","unstructured":"Sindhwani V, Keerthi S, Chapelle O (2006) Deterministic anealing for semi-supervised kernel machines. In: Proc. int. conf. mach. learn., pp 841\u2013848"},{"key":"2089_CR32","unstructured":"Sturm J (1999) SeDuMi 1.02, a matlab tool box for optimization over symmetric cones. Optim Methods Softw 11&12:625\u2013653"},{"issue":"4","key":"2089_CR33","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S0305-0483(01)00026-3","volume":"29","author":"F Tay","year":"2001","unstructured":"Tay F, Cao I (2001) Application of support vector machines in financial time series forecasting. Omega 29(4):309\u2013317","journal-title":"Omega"},{"key":"2089_CR34","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1137\/1038003","volume":"38","author":"L Vandenberghe","year":"1996","unstructured":"Vandenberghe L, Boyd S (1996) Semidefinite programming. SIAM Rev 38:49\u201395","journal-title":"SIAM Rev"},{"key":"2089_CR35","unstructured":"Vapnik V (1998) Statistical learning theory. Wiley-interscience, New York"},{"key":"2089_CR36","unstructured":"Xu L, Neufeld J, Larson B, Schuurmans D (2005) Maximum margin clustering. In: Saul L, Weiss Y, Bottou L (eds) Adv. neural info. process syst. MIT Press, Cambridge, pp 1537\u20131544"},{"key":"2089_CR37","unstructured":"Xu L, Jin R, Zhu K, King I, Lyu M (2008) Efficient convex relaxation for transductive support vector machine. In: Adv. neural info. process syst., vol 20. MIT Press, Cambridge, pp 1641\u20131648"},{"key":"2089_CR38","doi-asserted-by":"publisher","unstructured":"Yang X, Han L, Yan L, He L (2014) A bilateral-truncated-loss based robust support vector machine for classification problems. Soft Comput. doi: 10.1007\/s00500-014-1448-9","DOI":"10.1007\/s00500-014-1448-9"},{"key":"2089_CR39","doi-asserted-by":"crossref","unstructured":"Zhao B, Wang F, Zhang C (2008) Cuts3vm: a fast semi-supervised svm algorithm. In: Proc. 14th ACM SIGKDD int. conf. knowl. discov. data min., pp 830\u2013838","DOI":"10.1145\/1401890.1401989"},{"key":"2089_CR40","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-01548-9","volume-title":"Introduction to semi-supervised learning","author":"X Zhu","year":"2009","unstructured":"Zhu X, Goldberg A (2009) Introduction to semi-supervised learning. Morgan and Claypool, New York"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-016-2089-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-016-2089-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-016-2089-y","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-016-2089-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T22:55:18Z","timestamp":1692226518000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-016-2089-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,2]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1]]}},"alternative-id":["2089"],"URL":"https:\/\/doi.org\/10.1007\/s00500-016-2089-y","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,3,2]]}}}