{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T09:31:34Z","timestamp":1729675894054,"version":"3.28.0"},"reference-count":15,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,6]]},"DOI":"10.1109\/prni.2016.7552350","type":"proceedings-article","created":{"date-parts":[[2016,9,1]],"date-time":"2016-09-01T22:57:07Z","timestamp":1472770627000},"page":"1-4","source":"Crossref","is-referenced-by-count":1,"title":["Fast brain decoding with random sampling and random projections"],"prefix":"10.1109","author":[{"given":"Andres","family":"Hoyos-Idrobo","sequence":"first","affiliation":[]},{"given":"Gael","family":"Varoquaux","sequence":"additional","affiliation":[]},{"given":"Bertrand","family":"Thirion","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"682","article-title":"Using the nyström method to speed up kernel machines","author":"williams","year":"2001","journal-title":"NIPS"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1126\/science.1063736"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1162\/jocn.2007.19.9.1498"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.033"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.04.127"},{"key":"ref15","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.02.018"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.07.073"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0803205106"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487623"},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1090\/conm\/026\/737400","article-title":"Extensions of Lipschitz mappings into a Hilbert space","volume":"26","author":"johnson","year":"1984","journal-title":"Conference in modern analysis and probability"},{"key":"ref7","article-title":"Revisiting the nystrom method for improved large-scale machine learning","volume":"28","author":"gittens","year":"2013","journal-title":"ICML"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2005.06.070"},{"key":"ref1","article-title":"Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data","volume":"19","author":"o\u2019","year":"2007","journal-title":"J Cogn Neurosci"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-0000(03)00025-4"}],"event":{"name":"2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","start":{"date-parts":[[2016,6,22]]},"location":"Trento, Italy","end":{"date-parts":[[2016,6,24]]}},"container-title":["2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7551945\/7552322\/07552350.pdf?arnumber=7552350","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,24]],"date-time":"2017-06-24T17:49:20Z","timestamp":1498326560000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7552350\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6]]},"references-count":15,"URL":"https:\/\/doi.org\/10.1109\/prni.2016.7552350","relation":{},"subject":[],"published":{"date-parts":[[2016,6]]}}}