000131432 001__ 131432
000131432 005__ 20240209155915.0
000131432 0248_ $$2sideral$$a89009
000131432 037__ $$aART-2014-89009
000131432 041__ $$aeng
000131432 100__ $$0(orcid)0000-0002-6741-844X$$aMartinez-Cantin, R.
000131432 245__ $$aBayesOpt: A Bayesian optimization library for nonlinear optimization, experimental design and bandits
000131432 260__ $$c2014
000131432 5060_ $$aAccess copy available to the general public$$fUnrestricted
000131432 5203_ $$aBayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization characterized for being sample ecient as it builds a posterior distribution to capture the evidence and prior knowledge of the target function. Built in standard C++, the library is extremely ecient while being portable and
exible. It includes a common interface for C, C++, Python, Matlab and Octave.
000131432 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000131432 590__ $$a2.473$$b2014
000131432 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b22 / 123 = 0.179$$c2014$$dQ1$$eT1
000131432 591__ $$aAUTOMATION & CONTROL SYSTEMS$$b10 / 58 = 0.172$$c2014$$dQ1$$eT1
000131432 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000131432 773__ $$g15 (2014), 3735-3739$$pJ MACH LEARN RES$$tJOURNAL OF MACHINE LEARNING RESEARCH$$x1532-4435
000131432 8564_ $$s241921$$uhttps://zaguan.unizar.es/record/131432/files/texto_completo.pdf$$yVersión publicada
000131432 8564_ $$s1935073$$uhttps://zaguan.unizar.es/record/131432/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000131432 909CO $$ooai:zaguan.unizar.es:131432$$particulos$$pdriver
000131432 951__ $$a2024-02-09-14:28:02
000131432 980__ $$aARTICLE