{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T09:22:39Z","timestamp":1773739359797,"version":"3.50.1"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"3-4","license":[{"start":{"date-parts":[[2012,7,12]],"date-time":"2012-07-12T00:00:00Z","timestamp":1342051200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2013,9]]},"DOI":"10.1007\/s00521-012-1022-2","type":"journal-article","created":{"date-parts":[[2012,7,11]],"date-time":"2012-07-11T07:58:19Z","timestamp":1341993499000},"page":"975-987","source":"Crossref","is-referenced-by-count":6,"title":["A primal method for multiple kernel learning"],"prefix":"10.1007","volume":"23","author":[{"given":"Zhifeng","family":"Hao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ganzhao","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,7,12]]},"reference":[{"key":"1022_CR1","unstructured":"Argyriou A, Herbster M, Pontil M (2005) Combining graph laplacians for semi-supervised learning. In: NIPS, pp 67\u201374"},{"key":"1022_CR2","doi-asserted-by":"crossref","unstructured":"Argyriou A, Micchelli CA, Pontil M (2005) Learning convex combinations of continuously parameterized basic kernels. In: COLT, pp 338\u2013352","DOI":"10.1007\/11503415_23"},{"key":"1022_CR3","first-page":"1179","volume":"9","author":"FR Bach","year":"2008","unstructured":"Bach FR (2008) Consistency of the group lasso and multiple kernel learning. J Mach Learn Res 9:1179\u20131225","journal-title":"J Mach Learn Res"},{"key":"1022_CR4","doi-asserted-by":"crossref","unstructured":"Bach FR, Lanckriet GRG, Jordan MI (2004) Multiple kernel learning, conic duality, and the SMO algorithm. In: ICML, vol 69","DOI":"10.1145\/1015330.1015424"},{"key":"1022_CR5","unstructured":"Bertsekas D (1999) Nonlinear programming"},{"issue":"4","key":"1022_CR6","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1162\/neco.2007.19.4.1082","volume":"19","author":"L Bo","year":"2007","unstructured":"Bo L, Wang L, Jiao L (2007) Recursive finite newton algorithm for support vector regression in the primal. Neural Comput 19(4):1082\u20131096","journal-title":"Neural Comput"},{"key":"1022_CR7","doi-asserted-by":"crossref","unstructured":"Chapelle O (2007) Training a support vector machine in the primal. Neural Comput 19(5)","DOI":"10.1162\/neco.2007.19.5.1155"},{"key":"1022_CR8","unstructured":"Cortes C, Mohri M, Rostamizadeh A (2009) L2 regularization for learning kernels. In: UAI, pp 109\u2013116"},{"key":"1022_CR9","doi-asserted-by":"crossref","unstructured":"Duchi J, Shwartz SS, Singer Y, Chandra T (2008) Efficient projections onto the L1-ball for learning in high dimensions. In: ICML, pp 272\u2013279","DOI":"10.1145\/1390156.1390191"},{"issue":"3","key":"1022_CR10","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s00186-007-0161-1","volume":"66","author":"J Gorski","year":"2007","unstructured":"Gorski J, Pfeuffer F, Klamroth K (2007) Biconvex sets and optimization with biconvex functions: a survey and extensions. Math Methods Oper Res 66(3):373\u2013407","journal-title":"Math Methods Oper Res"},{"issue":"3","key":"1022_CR11","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/S0167-6377(99)00074-7","volume":"26","author":"L Grippo","year":"2000","unstructured":"Grippo L, Sciandrone M (2000) On the convergence of the block nonlinear gauss-seidel method under convex constraints. Oper Res Lett 26(3):127\u2013136","journal-title":"Oper Res Lett"},{"key":"1022_CR12","first-page":"1493","volume":"7","author":"SS Keerthi","year":"2006","unstructured":"Keerthi SS, Chapelle O, Decoste D (2006) Building support vector machines with reduced classifier complexity. J Mach Learn Res 7:1493\u20131515","journal-title":"J Mach Learn Res"},{"key":"1022_CR13","doi-asserted-by":"crossref","DOI":"10.1137\/1.9781611970920","volume-title":"Iterative methods for optimization. Frontiers in Applied Mathematics","author":"CT Kelley","year":"1999","unstructured":"Kelley CT (1999) Iterative methods for optimization. Frontiers in applied mathematics. SIAM, Thailand"},{"key":"1022_CR14","unstructured":"Kloft M, Brefeld U, Sonnenburg S, Laskov P, M\u00fcller K-R, Zien A (2009) Efficient and accurate Lp-Norm multiple kernel learning. In: NIPS, pp 997\u20131005"},{"key":"1022_CR15","first-page":"27","volume":"5","author":"GRG Lanckriet","year":"2004","unstructured":"Lanckriet GRG, Cristianini N, Bartlett P, Ghaoui LE, Jordan MI (2004) Learning the kernel matrix with semidefinite programming. J Mach Learn Res 5:27\u201372","journal-title":"J Mach Learn Res"},{"key":"1022_CR16","first-page":"1149","volume":"12","author":"S Melacci","year":"2011","unstructured":"Melacci S, Belkin M (2011) Laplacian support vector machines trained in the primal. J Mach Learn Res 12:1149\u20131184","journal-title":"J Mach Learn Res"},{"key":"1022_CR17","volume-title":"Introductory lectures on convex optimization: a basic course, volume 87 of applied optimization","author":"YE Nesterov","year":"2003","unstructured":"Nesterov YE (2003) Introductory lectures on convex optimization: a basic course, volume 87 of applied optimization. Kluwer, Boston"},{"key":"1022_CR18","first-page":"2491","volume":"9","author":"A Rakotomamonjy","year":"2008","unstructured":"Rakotomamonjy A, Bach FR, Canu S, Grandvalet Y (2008) Simple MKL. J Mach Learn Res 9:2491\u20132521","journal-title":"J Mach Learn Res"},{"key":"1022_CR19","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4175.001.0001","volume-title":"Learning with kernels: support vector machines, regularization, optimization, and beyond (adaptive computation and machine learning)","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond (adaptive computation and machine learning). The MIT Press, Cambridge"},{"key":"1022_CR20","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511809682","volume-title":"Kernel methods for pattern analysis","author":"J Shawe-Taylor","year":"2004","unstructured":"Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge"},{"key":"1022_CR21","first-page":"1531","volume":"7","author":"S Sonnenburg","year":"2006","unstructured":"Sonnenburg S, R\u00e4tsch G, Sch\u00e4fer C, Sch\u00f6lkopf B (2006) Large scale multiple kernel learning. J Mach Learn Res 7:1531\u20131565","journal-title":"J Mach Learn Res"},{"key":"1022_CR22","doi-asserted-by":"crossref","unstructured":"Varma M, Babu BR (2009) More generality in efficient multiple kernel learning. In: ICML, pp 1065\u20131072","DOI":"10.1145\/1553374.1553510"},{"key":"1022_CR23","unstructured":"Vishwanathan SVN, Sun Z, Theera-Ampornpunt N, Varma M (December 2010) Multiple kernel learning and the SMO algorithm. In: NIPS"},{"key":"1022_CR24","doi-asserted-by":"crossref","unstructured":"Zien A, Ong CS (2007) Multiclass multiple kernel learning. In: ICML, pp 1191\u20131198","DOI":"10.1145\/1273496.1273646"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-012-1022-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-012-1022-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-012-1022-2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T20:56:26Z","timestamp":1743713786000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-012-1022-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,7,12]]},"references-count":24,"journal-issue":{"issue":"3-4","published-print":{"date-parts":[[2013,9]]}},"alternative-id":["1022"],"URL":"https:\/\/doi.org\/10.1007\/s00521-012-1022-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,7,12]]}}}