{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T08:10:35Z","timestamp":1759133435638,"version":"3.37.3"},"reference-count":27,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2017,11,1]],"date-time":"2017-11-01T00:00:00Z","timestamp":1509494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["KRF-2013006599"],"award-info":[{"award-number":["KRF-2013006599"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006465","name":"Korea Ministry of Culture, Sports and Tourism (MCST) and Korea Creative Content Agency (KOCCA) in the Culture Technology(CT) Research & Development Program 2015","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006465","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cybern."],"published-print":{"date-parts":[[2017,11]]},"DOI":"10.1109\/tcyb.2016.2565683","type":"journal-article","created":{"date-parts":[[2016,5,22]],"date-time":"2016-05-22T00:55:36Z","timestamp":1463878536000},"page":"4003-4009","source":"Crossref","is-referenced-by-count":6,"title":["Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1792-0327","authenticated-orcid":false,"given":"Nojun","family":"Kwak","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"631","article-title":"Making large-scale Nystr&#x00F6;m approximation possible","author":"li","year":"2010","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","first-page":"567","article-title":"Revisiting Nystr&#x00F6;m method for improved large-scale machine learning","author":"gittens","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2359798"},{"key":"ref13","first-page":"243","article-title":"Efficient SVM training using low-rank kernel representations","volume":"2","author":"fine","year":"2002","journal-title":"J Mach Learn Res"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102356"},{"key":"ref15","first-page":"335","article-title":"Sampling techniques for kernel methods","author":"achlioptas","year":"2002","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/TSMCB.2007.914446","article-title":"On using prototype reduction schemes to optimize kernel-based fisher discriminant analysis","volume":"38","author":"kim","year":"2008","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2012.2236828"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2272292"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.33"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.896668"},{"journal-title":"UCI repository of machine learning databases","year":"1998","author":"newman","key":"ref27"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/FGR.2006.67","article-title":"Incremental kernel SVD for face recognition with image sets","author":"chin","year":"2006","journal-title":"Proc 7th Int Conf Autom Face Gesture Recognit"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1080\/00207160802044118"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15246-7_45"},{"key":"ref8","first-page":"682","article-title":"Using the Nystr&#x00F6;m method to speed up kernel machines","author":"williams","year":"2001","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-010-0189-3"},{"journal-title":"Kernel Methods for Pattern Analysis","year":"2004","author":"cristianini","key":"ref2"},{"key":"ref9","first-page":"2153","article-title":"On the Nystr&#x00F6;m method for approximating a gram matrix for improved kernel-based learning","volume":"6","author":"drineas","year":"2005","journal-title":"J Mach Learn Res"},{"journal-title":"Learning With Kernels Support Vector Machines Regularization Optimization and Beyond","year":"2002","author":"sch\u00f6lkopf","key":"ref1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2010.87"},{"key":"ref22","first-page":"27","article-title":"Incremental singular value decomposition algorithms for highly scalable recommender systems","author":"sarwar","year":"2002","journal-title":"Proc 5th Int Conf Comput Inf"},{"key":"ref21","first-page":"154","article-title":"Mercer&#x2019;s theorem, feature maps, and smoothing","author":"mihn","year":"2006","journal-title":"Learning Theory"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.382985"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.1262185"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/83.855432"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2365513"}],"container-title":["IEEE Transactions on Cybernetics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221036\/8067607\/07475867.pdf?arnumber=7475867","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T16:27:07Z","timestamp":1642004827000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/7475867\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11]]},"references-count":27,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tcyb.2016.2565683","relation":{},"ISSN":["2168-2267","2168-2275"],"issn-type":[{"type":"print","value":"2168-2267"},{"type":"electronic","value":"2168-2275"}],"subject":[],"published":{"date-parts":[[2017,11]]}}}