{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:34:42Z","timestamp":1787013282401,"version":"build-2736575974"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61171138"],"award-info":[{"award-number":["61171138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1109\/tnnls.2017.2782711","type":"journal-article","created":{"date-parts":[[2018,1,5]],"date-time":"2018-01-05T14:26:43Z","timestamp":1515162403000},"page":"4894-4904","source":"Crossref","is-referenced-by-count":21,"title":["A Two-Layer Mixture Model of Gaussian Process Functional Regressions and Its MCMC EM Algorithm"],"prefix":"10.1109","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4044-3530","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4490-1439","authenticated-orcid":false,"given":"Jinwen","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1214\/09-SS054"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1002\/er.787"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.97"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-041715-033624"},{"key":"ref31","first-page":"3011","article-title":"Gaussian processes for machine learning (GPML) toolbox","volume":"11","author":"rasmussen","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/0021-9045(72)90080-9"},{"key":"ref37","first-page":"1095","article-title":"A dynamic model selection algorithm for mixtures of Gaussian processes","author":"zhao","year":"2016","journal-title":"Proc IEEE 13th Int Conf Signal Process (ICSP)"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.03.010"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.12.028"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TSMCB.2006.870633","article-title":"A cost-function approach to rival penalized competitive learning (RPCL)","volume":"36","author":"ma","year":"2006","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"key":"ref10","first-page":"883","article-title":"An alternative infinite mixture of Gaussian process experts","volume":"18","author":"meeds","year":"2006","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2009.0108"},{"key":"ref11","first-page":"881","article-title":"Infinite mixtures of Gaussian process experts","volume":"2","author":"rasmussen","year":"2002","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref12","first-page":"1126","article-title":"Hierarchical double Dirichlet process mixture of Gaussian processes","author":"tayal","year":"2012","journal-title":"Proc Assoc Adv Artif Intell (AAAI)"},{"key":"ref13","first-page":"1011","article-title":"Infinite mixtures of multivariate Gaussian processes","author":"sun","year":"2013","journal-title":"Proc Int Conf Mach Learn Cybern (ICMLC)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300014764"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"dempster","year":"1977","journal-title":"J Roy Statist Soc B Statist Methodol"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2093575"},{"key":"ref17","first-page":"145","article-title":"Fast allocation of Gaussian process experts","author":"nguyen","year":"2014","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref18","first-page":"1897","article-title":"Variational mixture of Gaussian process experts","volume":"21","author":"yuan","year":"2008","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref19","first-page":"165","article-title":"An efficient EM approach to parameter learning of the mixture of Gaussian processes","volume":"6676","author":"yang","year":"2011","journal-title":"Adv Neural Networks"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2004.841879"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2301951"},{"key":"ref27","first-page":"294","article-title":"A DAEM algorithm for mixtures of Gaussian process functional regressions","volume":"9773","author":"wu","year":"2016","journal-title":"Proc 12th Int Conf Intell Comput (ICIC)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2300701"},{"key":"ref6","first-page":"633","article-title":"An alternative model for mixtures of experts","volume":"7","author":"xu","year":"1995","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref29","first-page":"335","article-title":"Automatic model selection of the mixtures of Gaussian processes for regression","volume":"9377","author":"qiang","year":"2015","journal-title":"Adv Neural Networks"},{"key":"ref5","first-page":"654","article-title":"Mixtures of Gaussian processes","volume":"13","author":"tresp","year":"2000","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2014.12.028"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00154"},{"key":"ref2","first-page":"2115","article-title":"Online regression for data with changepoints using Gaussian processes and reusable models","volume":"28","author":"grande","year":"2016","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref9","first-page":"2048","article-title":"Online learning of hierarchical Pitman&#x2013;Yor process mixture of generalized Dirichlet distributions with feature selection","volume":"28","author":"fan","year":"2016","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref1","author":"rasmussen","year":"2006","journal-title":"Gaussian Processes for Machine Learning"},{"key":"ref20","first-page":"68","article-title":"A precise hard-cut EM algorithm for mixtures of Gaussian processes","volume":"8589","author":"chen","year":"2014","journal-title":"Proc Int Conf Intell Comput (ICIC)"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0163004"},{"key":"ref21","first-page":"13","article-title":"The hard-cut EM algorithm for mixture of sparse Gaussian processes","volume":"9227","author":"chen","year":"2015","journal-title":"Proc Int Conf Intell Comput (ICIC)"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1990.10474930"},{"key":"ref23","first-page":"327","article-title":"An MCMC based EM algorithm for mixtures of Gaussian processes","volume":"9377","author":"wu","year":"2015","journal-title":"Adv Neural Networks"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-008-9055-1"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2007.00758.x"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/8468244\/08248656.pdf?arnumber=8248656","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T05:16:48Z","timestamp":1719724608000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8248656\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":40,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2017.2782711","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10]]}}}