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pro vyhledávání: '"gaussiset prosessit"'
For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data and an optimizer, e.g. a multiobjectiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b81c9a3e3b4e46da7eef06c74f626f9
http://urn.fi/URN:NBN:fi:jyu-202306133715
http://urn.fi/URN:NBN:fi:jyu-202306133715
In offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be ut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5928d01c1c9616934d8fabf43296addb
http://urn.fi/URN:NBN:fi:jyu-202210054798
http://urn.fi/URN:NBN:fi:jyu-202210054798
Autor:
Uusitalo, Sakari
Työn tavoitteena oli tarkastella sopiiko Gaussin prosessi koneoppimismentelmänä rakennuksista kerättävän aikasarjadatan perusteella tehtävään mallintamiseen ja voidaanko sitä hyödyntää kolmeen kerrostaloon tehtyjen energiatehokkuustoimen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4853::57d4115efab831ee331af1af5c250bee
https://trepo.tuni.fi/handle/10024/125014
https://trepo.tuni.fi/handle/10024/125014
Publikováno v:
Bayesian Anal. 15, no. 4 (2020), 1251-1280
Bayesian Analysis
Bayesian Analysis
Expert assessments are routinely used to inform management and other decision making. However, often these assessments contain considerable biases and uncertainties for which reason they should be calibrated if possible. Moreover, coherently combinin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d9ea00877c5239d3597b7b5a6f87657
https://doi.org/10.1214/19-ba1180
https://doi.org/10.1214/19-ba1180
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030637095
BIOMA
BIOMA
We propose a framework for solving offline data-driven multiobjective optimization problems in an interactive manner. No new data becomes available when solving offline problems. We fit surrogate models to the data to enable optimization, which intro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e69c0b59f13528212a423257ecbf5e9
http://urn.fi/URN:NBN:fi:jyu-202011266788
http://urn.fi/URN:NBN:fi:jyu-202011266788