001386611 000__ 02722cam\a22004934a\4500 001386611 001__ 1386611 001386611 003__ MaCbMITP 001386611 005__ 20240325105101.0 001386611 006__ m\\\\\o\\d\\\\\\\\ 001386611 007__ cr\cn\nnnunnun 001386611 008__ 060504s2006\\\\maua\\\\ob\\\\001\0\eng\d 001386611 020__ $$a9780262256834$$q(electronic bk.) 001386611 020__ $$a0262256835$$q(electronic bk.) 001386611 020__ $$a1423769902$$q(electronic bk.) 001386611 020__ $$a9781423769903$$q(electronic bk.) 001386611 020__ $$a9780262182539 001386611 020__ $$a026218253X 001386611 035__ $$a(OCoLC)68194203$$z(OCoLC)70924926$$z(OCoLC)182530415$$z(OCoLC)473741545$$z(OCoLC)475333793$$z(OCoLC)568000702$$z(OCoLC)648225626$$z(OCoLC)697661100$$z(OCoLC)874323084$$z(OCoLC)888539648$$z(OCoLC)958086640$$z(OCoLC)958393477$$z(OCoLC)961519344$$z(OCoLC)962716684$$z(OCoLC)966210716$$z(OCoLC)988415099$$z(OCoLC)992073833$$z(OCoLC)994915542$$z(OCoLC)1037434254$$z(OCoLC)1037937670$$z(OCoLC)1038668435$$z(OCoLC)1055374391$$z(OCoLC)1065075723$$z(OCoLC)1081252479$$z(OCoLC)1083596126 001386611 035__ $$a(OCoLC-P)68194203 001386611 040__ $$aOCoLC-P$$beng$$epn$$cOCoLC-P 001386611 050_4 $$aQA274.4$$b.R37 2006eb 001386611 072_7 $$aMAT$$x029040$$2bisacsh 001386611 08204 $$a519.2/3$$222 001386611 1001_ $$aRasmussen, Carl Edward. 001386611 24510 $$aGaussian processes for machine learning /$$cCarl Edward Rasmussen, Christopher K.I. Williams. 001386611 260__ $$aCambridge, Mass. :$$bMIT Press,$$c©2006. 001386611 300__ $$a1 online resource (xviii, 248 pages) :$$billustrations. 001386611 336__ $$atext$$btxt$$2rdacontent 001386611 337__ $$acomputer$$bc$$2rdamedia 001386611 338__ $$aonline resource$$bcr$$2rdacarrier 001386611 4901_ $$aAdaptive computation and machine learning 001386611 5201_ $$a"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics."--Jacket. 001386611 588__ $$aOCLC-licensed vendor bibliographic record. 001386611 650_0 $$aGaussian processes$$xData processing. 001386611 650_0 $$aMachine learning$$xMathematical models. 001386611 653__ $$aCOMPUTER SCIENCE/Machine Learning & Neural Networks 001386611 655_0 $$aElectronic books 001386611 7001_ $$aWilliams, Christopher K. I. 001386611 852__ $$bebk 001386611 85640 $$3MIT Press$$uhttps://doi.org/10.7551/mitpress/3206.001.0001?locatt=mode:legacy$$zOnline Access through The MIT Press Direct 001386611 85642 $$3OCLC metadata license agreement$$uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf 001386611 909CO $$ooai:library.usi.edu:1386611$$pGLOBAL_SET 001386611 980__ $$aBIB 001386611 980__ $$aEBOOK 001386611 982__ $$aEbook 001386611 983__ $$aOnline