{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:03:36Z","timestamp":1780445016150,"version":"3.54.1"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004052","name":"King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research","doi-asserted-by":"publisher","award":["URF\/1\/4098-01-01"],"award-info":[{"award-number":["URF\/1\/4098-01-01"]}],"id":[{"id":"10.13039\/501100004052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004052","name":"King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research","doi-asserted-by":"publisher","award":["BAS\/1\/1624-01-01"],"award-info":[{"award-number":["BAS\/1\/1624-01-01"]}],"id":[{"id":"10.13039\/501100004052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61703396"],"award-info":[{"award-number":["61703396"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673293"],"award-info":[{"award-number":["61673293"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CCF-Tencent Open Fund and Shenzhen Government","award":["GJHZ20180419190732022"],"award-info":[{"award-number":["GJHZ20180419190732022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1109\/tnnls.2019.2958922","type":"journal-article","created":{"date-parts":[[2020,1,14]],"date-time":"2020-01-14T20:58:39Z","timestamp":1579035519000},"page":"4881-4891","source":"Crossref","is-referenced-by-count":22,"title":["Approximate Kernel Selection via Matrix Approximation"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6464-310X","authenticated-orcid":false,"given":"Lizhong","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0594-7116","authenticated-orcid":false,"given":"Shizhong","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6739-621X","authenticated-orcid":false,"given":"Yong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0337-9410","authenticated-orcid":false,"given":"Yazhou","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8264-6117","authenticated-orcid":false,"given":"Ling","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7108-3574","authenticated-orcid":false,"given":"Xin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","first-page":"113","article-title":"On the impact of kernel approximation on learning accuracy","author":"cortes","year":"2010","journal-title":"Proc 14th Int Conf Artif Intell Statist (AISTATS)"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0803205106"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2838140"},{"key":"ref32","first-page":"567","article-title":"Revisiting the Nyström method for improved large-scale machine learning","author":"gittens","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn (ICML)"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2064786"},{"key":"ref30","first-page":"981","article-title":"Sampling methods for the Nyström method","volume":"13","author":"kumar","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1137\/07070471X"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2844242"},{"key":"ref35","first-page":"996","article-title":"Efficient algorithms and error analysis for the modified Nyström method","author":"wang","year":"2014","journal-title":"Proc 14th Int Conf Artif Intell Statist (AISTATS)"},{"key":"ref34","first-page":"2729","article-title":"Improving CUR matrix decomposition and the Nyström approximation via adaptive sampling","volume":"14","author":"wang","year":"2013","journal-title":"J Mach Learn Res"},{"key":"ref28","first-page":"682","article-title":"Using the Nyström method to speed up kernel machines","volume":"13","author":"williams","year":"2001","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944959"},{"key":"ref29","first-page":"2153","article-title":"On the Nyström method for approximating a Gram matrix for improved kernel-based learning","volume":"6","author":"drineas","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref2","author":"alpaydin","year":"2004","journal-title":"Introduction to Machine Learning"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1090\/S0273-0979-01-00923-5"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2662034"},{"key":"ref22","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"cawley","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012450327387"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jco.2010.02.003"},{"key":"ref23","first-page":"2910","article-title":"Randomized kernel selection with spectra of multilevel circulant matrices","author":"ding","year":"2018","journal-title":"Proc AAAI Conf Artif Intell (AAAI)"},{"key":"ref26","first-page":"165","article-title":"Approximate model selection for large scale LSSVM","volume":"20","author":"ding","year":"2011","journal-title":"Journal of Machine Learning Research-Proc Track"},{"key":"ref25","article-title":"Approximation of kernel matrices in machine learning","author":"song","year":"2010"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1023\/A:1018946025316"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2011.21"},{"key":"ref55","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"dem\u0161ar","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553446"},{"key":"ref53","first-page":"185","article-title":"Sharp analysis of low-rank kernel matrix approximations","author":"bach","year":"2013","journal-title":"Proc of the Annual Conf on Learning Theory (COLT)"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/1109557.1109681"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2424684"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2513006"},{"key":"ref40","first-page":"1060","article-title":"Nyström method vs random Fourier features: A theoretical and empirical comparison","volume":"24","author":"yang","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref12","first-page":"324","article-title":"Efficient approximation of cross-validation for kernel methods using Bouligand influence function","author":"liu","year":"2014","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref13","first-page":"1099","article-title":"Learning the kernel function via regularization","volume":"6","author":"micchelli","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/346"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2892371"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013999503812"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2202401"},{"key":"ref18","first-page":"2760","article-title":"Learning kernels using local Rademacher complexity","author":"cortes","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref19","first-page":"1591","article-title":"Multi-class learning: From theory to algorithm","author":"li","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2873649"},{"key":"ref3","author":"mitchell","year":"1997","journal-title":"Machine Learning"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2851305"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2890021"},{"key":"ref8","first-page":"11257","article-title":"Two generator game: Learning to sample via linear goodness-of-fit test","author":"ding","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013454"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/0024-3795(94)00025-5"},{"key":"ref9","author":"sch\u00f6lkopf","year":"2002","journal-title":"Learning With Kernels Support Vector Machines Regularization Optimization and Beyond"},{"key":"ref46","first-page":"107","article-title":"Refinement of reproducing kernels","volume":"10","author":"xu","year":"2009","journal-title":"J Mach Learn Res"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2520582"},{"key":"ref48","author":"davis","year":"1979","journal-title":"Circulant Matrices"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177697089"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44848-9_23"},{"key":"ref41","first-page":"6939","article-title":"Improved bounds for the Nyström method with application to kernel classification","volume":"5","author":"jin","year":"2013","journal-title":"IEEE Trans Inf Theory"},{"key":"ref44","first-page":"282","article-title":"Nyström approximate model selection for LSSVM","author":"ding","year":"2012","journal-title":"Proc Adv Knowl Discovery Data Mining 6th Pacific–Asia Conf (PAKDD)"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013462"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/9244673\/08959405.pdf?arnumber=8959405","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T17:19:56Z","timestamp":1651079996000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8959405\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11]]},"references-count":55,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2019.2958922","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11]]}}}