{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T19:23:54Z","timestamp":1740165834229,"version":"3.37.3"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076138"],"award-info":[{"award-number":["62076138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2341229"],"award-info":[{"award-number":["U2341229"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Science and Technology Key Research and Development Project of China","award":["2023YFF0905400"],"award-info":[{"award-number":["2023YFF0905400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1109\/tnnls.2024.3354978","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T18:35:19Z","timestamp":1707503719000},"page":"2154-2168","source":"Crossref","is-referenced-by-count":3,"title":["Global Model Selection for Semi-Supervised Support Vector Machine via Solution Paths"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5317-1629","authenticated-orcid":false,"given":"Yajing","family":"Fan","sequence":"first","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1889-0163","authenticated-orcid":false,"given":"Shuyang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Michigan State University, East Lansing, MI, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7165-3143","authenticated-orcid":false,"given":"Bin","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University, Changchun, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1302-0567","authenticated-orcid":false,"given":"Ziran","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China"}]},{"given":"Zhou","family":"Zhai","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3483-8333","authenticated-orcid":false,"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"Heng Huang is the Department of Computer Science, University of Maryland, College Park, MD, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2697-8093","authenticated-orcid":false,"given":"Yi","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University, Changchun, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2012.2236683"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-19066-2_45"},{"key":"ref3","first-page":"203","article-title":"Optimization techniques for semi-supervised support vector machines","volume":"9","author":"Chapelle","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220092"},{"key":"ref5","first-page":"1687","article-title":"Large scale transductive SVMs","volume":"7","author":"Collobert","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2006.877950"},{"issue":"Oct","key":"ref7","first-page":"1391","article-title":"The entire regularization path for the support vector machine","volume":"5","author":"Hastie","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2009.2039000"},{"key":"ref9","article-title":"Suboptimal solution path algorithm for support vector machine","author":"Karasuyama","year":"2011","journal-title":"arXiv:1105.0471"},{"key":"ref10","first-page":"1713","article-title":"Considering cost asymmetry in learning classifiers","volume":"7","author":"Bach","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref11","first-page":"3532","article-title":"Bi-parameter space partition for cost-sensitive SVM","volume-title":"Proc. 24th Int. Joint Conf. Artif. Intell.","author":"Gu"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2578326"},{"issue":"5","key":"ref13","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1109\/TNNLS.2012.2183644","article-title":"Regularization path for \u03bd-support vector classification","volume":"23","author":"Gu","year":"2012","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2527796"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2008.10-07-628"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1214\/009053606000001370"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2017.11.008"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2007.19.6.1633"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2002077"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098010"},{"article-title":"Model selection for semi-supervised learning with limited labeled data","year":"2012","author":"Quanz","key":"ref21"},{"key":"ref22","first-page":"1209","article-title":"Extensions to metric-based model selection","volume":"3","author":"Bengio","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref23","first-page":"1059","article-title":"Semi-supervised Gaussian process classifiers","volume-title":"Proc. IJCAI","author":"Sindhwani"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013947519741"},{"key":"ref25","first-page":"633","article-title":"Stability-based model selection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lange"},{"article-title":"Minima of functions of several variables with inequalities as side constraints","year":"1939","author":"Karush","key":"ref26"},{"key":"ref27","first-page":"2549","article-title":"A new generalized error path algorithm for model selection","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gu"},{"key":"ref28","article-title":"Large scale online learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"16","author":"Bottou"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2011.2106219"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1162\/08997660360581958"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2771456"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330962"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2250300"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10877690\/10431549.pdf?arnumber=10431549","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T06:44:03Z","timestamp":1738910643000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10431549\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":34,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3354978","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2025,2]]}}}