{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:03:15Z","timestamp":1760608995112,"version":"3.37.3"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100007488","name":"Indian Institute of Technology Delhi (IIT Delhi) through the Microsoft Chair Professor Project","doi-asserted-by":"publisher","award":["MI01158"],"award-info":[{"award-number":["MI01158"]}],"id":[{"id":"10.13039\/501100007488","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007488","name":"IIT Delhi HPC Facility for Computational Resources","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007488","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Syst. Man Cybern, Syst."],"published-print":{"date-parts":[[2017,10]]},"DOI":"10.1109\/tsmc.2017.2694321","type":"journal-article","created":{"date-parts":[[2017,6,7]],"date-time":"2017-06-07T18:10:17Z","timestamp":1496859017000},"page":"2653-2662","source":"Crossref","is-referenced-by-count":10,"title":["Large-Scale Minimal Complexity Machines Using Explicit Feature Maps"],"prefix":"10.1109","volume":"47","author":[{"given":"Mayank","family":"Sharma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0604-8756","authenticated-orcid":false,"family":"Jayadeva","sequence":"additional","affiliation":[]},{"given":"Sumit","family":"Soman","sequence":"additional","affiliation":[]},{"given":"Himanshu","family":"Pant","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150429"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273598"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-014-9255-2"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.130"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2007.19.5.1155"},{"key":"ref30","first-page":"301","article-title":"Support vector machine solvers","author":"bottou","year":"2007","journal-title":"Large Scale Kernel Machines"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390208"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.113.130503"},{"key":"ref35","first-page":"3689","article-title":"Fast prediction for large-scale kernel machines","author":"hsieh","year":"2014","journal-title":"Proc 27th Int Conf Neural Inf Process Syst (NIPS)"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.03.113"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref60"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177730491"},{"key":"ref61","first-page":"265","article-title":"On the algorithmic implementation of multiclass kernel-based vector machines","volume":"2","author":"crammer","year":"2002","journal-title":"J Mach Learn Res"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1214\/09-SS051"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2006.875989"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"ref29","first-page":"388","article-title":"Incremental and decremental support vector machine learning","author":"cauwenberghs","year":"2000","journal-title":"Proc NIPS"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.06.065"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.07.062"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2013.823"},{"journal-title":"Advances in Kernel Methods","year":"1999","author":"joachims","key":"ref22"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.07.010"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273611"},{"key":"ref23","first-page":"363","article-title":"Core vector machines: Fast SVM training on very large data sets","volume":"6","author":"tsang","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390170"},{"key":"ref25","first-page":"1579","article-title":"Fast kernel classifiers with online and active learning","volume":"6","author":"bordes","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref50","first-page":"3041","article-title":"Scalable kernel methods via doubly stochastic gradients","author":"dai","year":"2014","journal-title":"Proc 27th Int Conf Neural Inf Process Syst (NIPS)"},{"journal-title":"Statistical Learning Theory","year":"1998","author":"vapnik","key":"ref51"},{"key":"ref59","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref58","first-page":"476","article-title":"Nystr&#x00F6;m method vs random Fourier features: A theoretical and empirical comparison","author":"yang","year":"2012","journal-title":"Proc 25th Int Conf Neural Inf Process Syst (NIPS)"},{"key":"ref57","first-page":"862","article-title":"On the error of random Fourier features","author":"sutherland","year":"2015","journal-title":"Proc UAI"},{"journal-title":"Generalization properties of learning with random features","year":"2017","author":"rudi","key":"ref56"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1112\/jlms\/s1-39.1.187"},{"key":"ref54","first-page":"189","article-title":"The representer theorem for Hilbert spaces: A necessary and sufficient condition","author":"dinuzzo","year":"2012","journal-title":"Proc 25th Int Conf Neural Inf Process Syst (NIPS)"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44581-1_27"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009715923555"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2012.2201465"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.E92.D.1338"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/1148170.1148253"},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/7496.001.0001","author":"bottou","year":"2007","journal-title":"Large-Scale Kernel Machines"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012454411458"},{"key":"ref14","first-page":"911","article-title":"Sparse greedy matrix approximation for machine learning","author":"smola","year":"2000","journal-title":"Proc ICML"},{"key":"ref15","first-page":"661","article-title":"Using the nystr&#x00F6;m method to speed up kernel machines","author":"williams","year":"2000","journal-title":"Proc 13th Int Conf Neural Inf Process Syst (NIPS)"},{"key":"ref16","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":"ref17","first-page":"113","article-title":"On the impact of kernel approximation on learning accuracy","volume":"9","author":"cortes","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Stat"},{"key":"ref18","first-page":"185","author":"platt","year":"1999","journal-title":"Advances in Kernel Methods"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1162\/089976601300014493"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2016.2560126"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2015.7280663"},{"key":"ref6","first-page":"985","article-title":"Locally linear support vector machines","author":"ladick\u00fd","year":"2011","journal-title":"Proc 28th Int Conf Mach Learn (ICML)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2016.2523930"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2012.2226575"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2013.2295353"},{"key":"ref49","first-page":"244","article-title":"Fastfood&#x2014;Computing Hilbert space expansions in loglinear time","volume":"28","author":"le","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2012.2224338"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2016.2635141"},{"key":"ref45","first-page":"876","article-title":"Accelerating support vector machine learning with GPU-based mapreduce","author":"sun","year":"2015","journal-title":"Proc IEEE Int Conf Syst Man Cybern"},{"key":"ref48","first-page":"1177","article-title":"Random features for large-scale kernel machines","author":"rahimi","year":"2007","journal-title":"Proc 20th Int Conf Neural Inf Process Syst (NIPS)"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2614809"},{"key":"ref42","first-page":"1871","article-title":"Liblinear: A library for large linear classification","volume":"9","author":"fan","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref41","first-page":"1369","article-title":"Coordinate descent method for large-scale l2-loss linear support vector machines","volume":"9","author":"chang","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref44","first-page":"566","article-title":"A divide-and-conquer solver for kernel support vector machines","author":"hsieh","year":"2014","journal-title":"Proc ICML"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2014.6974265"}],"container-title":["IEEE Transactions on Systems, Man, and Cybernetics: Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221021\/8038140\/07942005.pdf?arnumber=7942005","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T16:04:53Z","timestamp":1642003493000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7942005\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10]]},"references-count":63,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tsmc.2017.2694321","relation":{},"ISSN":["2168-2216","2168-2232"],"issn-type":[{"type":"print","value":"2168-2216"},{"type":"electronic","value":"2168-2232"}],"subject":[],"published":{"date-parts":[[2017,10]]}}}