{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T15:54:41Z","timestamp":1779292481579,"version":"3.51.4"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2014,9,3]],"date-time":"2014-09-03T00:00:00Z","timestamp":1409702400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2014,12]]},"DOI":"10.1007\/s10489-014-0563-8","type":"journal-article","created":{"date-parts":[[2014,9,2]],"date-time":"2014-09-02T02:19:50Z","timestamp":1409624390000},"page":"1059-1068","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Multi-view Laplacian twin support vector machines"],"prefix":"10.1007","volume":"41","author":[{"given":"Xijiong","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiliang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2014,9,3]]},"reference":[{"issue":"17","key":"563_CR1","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.1016\/j.neucom.2011.06.026","volume":"74","author":"J Shawe-Taylor","year":"2011","unstructured":"Shawe-Taylor J, Sun S (2011) A review of optimization methodologies in support vector machines. Neurocomputing 74(17):3609\u20133618","journal-title":"Neurocomputing"},{"key":"563_CR2","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"1995","unstructured":"Vapnik V (1995) The nature of statistical learning theory. Springer, New York"},{"key":"563_CR3","volume-title":"An introduction to ssupport vector machines","author":"N Christianini","year":"2002","unstructured":"Christianini N (2002) An introduction to ssupport vector machines. Cambridge University Press, Cambridge"},{"key":"563_CR4","doi-asserted-by":"crossref","DOI":"10.1007\/b12006","volume-title":"Learning with kernels","author":"B Scholkopf","year":"2003","unstructured":"Scholkopf B, Smola A (2003) Learning with kernels. MIT Press, Cambridge"},{"key":"563_CR5","unstructured":"Fung G, Mangasarian O (2001) Proximal support vector machines. In: Proceedings of the 7th international conference knowledge discovery and data mining, pp 77\u201386"},{"issue":"1","key":"563_CR6","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/TPAMI.2006.17","volume":"28","author":"O Mangasarian","year":"2006","unstructured":"Mangasarian O, Wild E (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69\u201374","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"563_CR7","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","volume":"29","author":"K Jayadeva","year":"2007","unstructured":"Jayadeva K, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905\u2013910","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"563_CR8","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.sigpro.2008.10.002","volume":"89","author":"S Ghorai","year":"2009","unstructured":"Ghorai S, Mukherjee A, Dutta P (2009) Nonparallel plane proximal classifier. Signal Process 89(4):510\u2013522","journal-title":"Signal Process"},{"key":"563_CR9","doi-asserted-by":"crossref","unstructured":"Shao Y, Chen W, Deng N (2013) Nonparallel hyperplane support vector machine for binary classification problems. Information sciences. doi: 10.1016\/j.ins.2013.11.003","DOI":"10.1016\/j.ins.2013.11.003"},{"issue":"3","key":"563_CR10","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s10489-013-0423-y","volume":"39","author":"Y Shao","year":"2013","unstructured":"Shao Y, Wang Z, Chen W, Deng N (2013) Least squares twin parametric-margin support vector machines for classification. Appl Intell 39(3):451\u2013464","journal-title":"Appl Intell"},{"key":"563_CR11","doi-asserted-by":"crossref","unstructured":"Xu Y, Guo R (2014) An improved \u03bd-twin support vector machine. Appled intelligence. doi: 10.1007\/s10489-013-0500-2","DOI":"10.1007\/s10489-013-0500-2"},{"key":"563_CR12","doi-asserted-by":"crossref","unstructured":"Chen W, Shao Y, Xu D, Fu Y (2013) Manifold proximal support vector machine for semi-supervised classification. Applied intelligence. doi: 10.1007\/s10489-013-0491-z","DOI":"10.1007\/s10489-013-0491-z"},{"issue":"5","key":"563_CR13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S0218001413500158","volume":"27","author":"Z Yang","year":"2013","unstructured":"Yang Z (2013) Nonparallel hyperplanes proximal classifiers based on manifold regularization for labeled and unlabeled examples. Int J Pattern Recogn Artif Intell 27(5):1\u201319","journal-title":"Int J Pattern Recogn Artif Intell"},{"key":"563_CR14","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.neunet.2011.08.003","volume":"25","author":"Y Shao","year":"2012","unstructured":"Shao Y, Deng N (2012) A coordinate descent margin based-twin support vector machine for classification. Neural Netw 25:114\u2013121","journal-title":"Neural Netw"},{"key":"563_CR15","volume-title":"Semi-supervised Learning","author":"O Chapelle","year":"2010","unstructured":"Chapelle O, Scholkopf B, Zien A (2010) Semi-supervised Learning. MIT Press, Massachusetts"},{"key":"563_CR16","unstructured":"Zhu X (2008) Semi-supervised learning literature survey. Technical report 1530, Department of Computer Sciences University of Wisconsin Madison"},{"key":"563_CR17","unstructured":"Zhu X, Ghahramani Z, Lafferty J (2006) Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the 20th international conference machine learning, pp 912\u2013 919"},{"key":"563_CR18","unstructured":"Zhou Z, Zhan D, Yang Q (2007) Semi-supervised learning with very few labeled training example. In: Proceedings of the 22nd AAAI conference on artificial intelligence, pp 675\u2013680"},{"key":"563_CR19","unstructured":"Joachims T (1999) Transductive inference for text classification using support vector machines. In: Proceedings of the 16th international conference on machine learning, pp 200\u2013209"},{"key":"563_CR20","first-page":"368","volume":"11","author":"K Bennett","year":"1999","unstructured":"Bennett K, Demiriz A (1999) Semi-supervised support vector machines. Adv Neural Info Proc Syst 11:368\u2013374","journal-title":"Adv Neural Info Proc Syst"},{"key":"563_CR21","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/10556780108805809","volume":"15","author":"G Fung","year":"2001","unstructured":"Fung G, Mangasarian O (2001) Semi-supervised support vector machines for unlabeled data classification. Optim Method Soft 15:29\u201344","journal-title":"Optim Method Soft"},{"key":"563_CR22","first-page":"2399","volume":"7","author":"M Belkin","year":"2006","unstructured":"Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399\u20132434","journal-title":"J Mach Learn Res"},{"key":"563_CR23","first-page":"1149","volume":"12","author":"S Melacci","year":"2011","unstructured":"Melacci S, Beklin M (2011) Laplacian support vector machines trained in the primal. J Mach Learn Res 12:1149\u20131184","journal-title":"J Mach Learn Res"},{"key":"563_CR24","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.neunet.2012.07.011","volume":"35","author":"Z Qi","year":"2012","unstructured":"Qi Z, Tian Y, Shi Y (2012) Laplacian twin support vector machine for semi-supervised classification. Neural Netw 35:46\u201353","journal-title":"Neural Netw"},{"issue":"6","key":"563_CR25","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1109\/TNN.2011.2130540","volume":"22","author":"Y Shao","year":"2011","unstructured":"Shao Y, Zhang C, Wang X, Deng N (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6):962\u2013968","journal-title":"IEEE Trans Neural Netw"},{"key":"563_CR26","doi-asserted-by":"crossref","unstructured":"Ding S, Zhao Y, Qi B, Huang H (2012) An overview on twin support vector machines. Artificial intelligence review. doi: 10.1007\/s10462-012-9336-0","DOI":"10.1007\/s10462-012-9336-0"},{"key":"563_CR27","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1007\/s00521-013-1362-6","volume":"23","author":"S Sun","year":"2013","unstructured":"Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23:2031\u20132038","journal-title":"Neural Comput Appl"},{"key":"563_CR28","doi-asserted-by":"crossref","unstructured":"Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th annual conference on computational learning theory, pp 92\u2013100","DOI":"10.1145\/279943.279962"},{"key":"563_CR29","doi-asserted-by":"crossref","unstructured":"Sindhwani V, Rosenberg D (2008) An RKHS for multi-view learning and manifold co-regularization. In: Proceedings of the 25th international conference on machine learning, pp 976\u2013983","DOI":"10.1145\/1390156.1390279"},{"key":"563_CR30","unstructured":"Sindhwani V, Niyogi P, Belkin M (2005) A co-regularization approach to semi-supervised learning with multiple views. In: Proceedings of the workshop on learning with multiple views, 22nd ICML, pp 824\u2013831"},{"key":"563_CR31","first-page":"355","volume":"18","author":"J Farquhar","year":"2006","unstructured":"Farquhar J, Hardoon D, Shawe-Taylor J, Szedmak S (2006) Two view learning: SVM-2K, theory and practice. Adv Neural Info Proc Syst 18:355\u2013362","journal-title":"Adv Neural Info Proc Syst"},{"key":"563_CR32","first-page":"2423","volume":"11","author":"S Sun","year":"2010","unstructured":"Sun S, Shawe-Taylor J (2010) Sparse semi-supervised learning using conjugate functions. J Mach Learn Res 11:2423\u20132455","journal-title":"J Mach Learn Res"},{"key":"563_CR33","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/978-3-642-25856-5_16","volume":"7121","author":"S Sun","year":"2011","unstructured":"Sun S (2011) Multi-view Laplacian support vector machines. Lect Notes Comput Sci 7121:209\u2013222","journal-title":"Lect Notes Comput Sci"},{"key":"563_CR34","first-page":"463","volume":"3","author":"P Bartlett","year":"2002","unstructured":"Bartlett P, Mendelson S (2002) Rademacher and Gaussian complexities: risk bounds and structural results. J Mach Learn Res 3:463\u2013482","journal-title":"J Mach Learn Res"},{"key":"563_CR35","doi-asserted-by":"crossref","unstructured":"Kushmerick N (1999) Learning to remove internet advertisement. In: Proceedings of the 3rd annual conference on autonomous agents, pp 175\u2013181","DOI":"10.1145\/301136.301186"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-014-0563-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-014-0563-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-014-0563-8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,14]],"date-time":"2019-08-14T11:14:38Z","timestamp":1565781278000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-014-0563-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,9,3]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2014,12]]}},"alternative-id":["563"],"URL":"https:\/\/doi.org\/10.1007\/s10489-014-0563-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,9,3]]}}}