{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:15:59Z","timestamp":1775801759104,"version":"3.50.1"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["106-2410-H-002-071-MY3"],"award-info":[{"award-number":["106-2410-H-002-071-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2021,6,1]]},"DOI":"10.1109\/tkde.2019.2953728","type":"journal-article","created":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T21:20:50Z","timestamp":1573852850000},"page":"2669-2679","source":"Crossref","is-referenced-by-count":35,"title":["Nonparametric Regression via Variance-Adjusted Gradient Boosting Gaussian Process Regression"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9735-3421","authenticated-orcid":false,"given":"Hsin-Min","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5592-7100","authenticated-orcid":false,"given":"Jih-Shin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2061-2309","authenticated-orcid":false,"given":"Wei-Chun","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","first-page":"591","article-title":"The million song dataset","author":"bertin-mahieux","year":"2011","journal-title":"Proc 12th Int Conf Music Inf Retrieval"},{"key":"ref32","article-title":"Generalized boosted models: A guide to the GBM package","author":"ridgeway","year":"2007"},{"key":"ref31","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"ref35","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref34","article-title":"GPy: A Gaussian process framework in python","year":"2012"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300014908"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2008.4650850"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000689"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2015.1027067"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970128"},{"key":"ref15","first-page":"282","article-title":"Gaussian processes for big data","author":"hensman","year":"2013","journal-title":"Proc Conf Uncertainty Artif Intell"},{"key":"ref16","first-page":"1939","article-title":"A unifying view of sparse approximate gaussian process regression","volume":"6","author":"qui\u00f1onero candela","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref17","author":"bishop","year":"2006","journal-title":"Pattern Recognition and Machine Learning (Information Science and Statistics)"},{"key":"ref18","first-page":"625","article-title":"Fast sparse gaussian process methods: The informative vector machine","author":"lawrence","year":"2002","journal-title":"Proc 15th Int Conf Neural Inf Process Syst"},{"key":"ref19","first-page":"643","article-title":"A matching pursuit approach to sparse Gaussian process regression","author":"keerthi","year":"2006","journal-title":"Proc 18th Int Conf Neural Inf Process Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2424873"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"friedman","year":"2001","journal-title":"The Ann Statist"},{"key":"ref27","first-page":"1303","article-title":"Stochastic variational inference","volume":"14","author":"hoffman","year":"2013","journal-title":"J Mach Learn Res"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.2113\/gsecongeo.58.8.1246"},{"key":"ref29","first-page":"5537","article-title":"Variational fourier features for Gaussian processes","volume":"18","author":"hensman","year":"2017","journal-title":"J Mach Learn Res"},{"key":"ref5","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2017.89"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2017.05.004"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2008.00663.x"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref20","first-page":"682","article-title":"Using the nyström method to speed up kernel machines","author":"williams","year":"2001","journal-title":"Proc 13th Int Conf Neural Inf Process Syst"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970128"},{"key":"ref21","article-title":"Observations on the nyström method for Gaussian processes","author":"williams","year":"2002","journal-title":"Tech Rep"},{"key":"ref24","first-page":"1257","article-title":"Sparse Gaussian processes using pseudo-inputs","author":"snelson","year":"2006","journal-title":"Proc 18th Int Conf Neural Inf Process Syst"},{"key":"ref23","article-title":"Fast forward selection to speed up sparse Gaussian process regression","author":"seeger","year":"2003","journal-title":"Proc 9th Int Workshop Artif Intell Statist"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"ref25","first-page":"16","article-title":"Variational learning of inducing variables in sparse Gaussian processes","author":"titsias","year":"0","journal-title":"Proc 12th Int Conf Artif Intell Statist"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/9427778\/08902053.pdf?arnumber=8902053","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:50:53Z","timestamp":1652194253000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8902053\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,1]]},"references-count":37,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2019.2953728","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,1]]}}}