Semi-parametric approach to random forests for high-dimensional bayesian optimisation
author
Kuzmanovski, Vladimir
Hollmén, Jaakko
statement of authorship
Vladimir Kuzmanovski and Jaakko Hollmén
source
Discovery Science : 25th International Conference, DS 2022, Montpellier, France, October 10–12, 2022, Proceedings
location of publication
Cham
publisher
Springer
year of publication
2022
pages
p. 418 - 428
series
Lecture notes in computer science ; 13601
conference name, date
25th International Conference on Discovery Science, DS 2022, 10-12 October 2022
conference location
Montpellier
url
https://doi.org/10.1007/978-3-031-18840-4_30
subject term
tehisõpe
optimeerimine
algoritmid
Scopus
https://www.scopus.com/sourceid/25674
https://www.scopus.com/record/display.uri?eid=2-s2.0-85142759430&origin=resultslist&sort=plf-f&src=s&sid=d9d37ad6b8048f0f9ccde42ffcc3a0a0&sot=b&sdt=b&s=AUTHOR-NAME%28%22Kuzmanovski%2C+Vladimir%22%29&sl=110&sessionSearchId=d9d37ad6b8048f0f9ccde42ffcc3a0a0&relpos=1
WOS
https://jcr.clarivate.com/jcr-jp/journal-profile?journal=LECT%20NOTES%20ARTIF%20INT&year=2005
https://www.webofscience.com/wos/woscc/full-record/WOS:000897761100030
quartile
Q3
category (general)
Computer science
Arvutiteadus
Mathematics
Matemaatika
category (sub)
Computer science. General computer science
Arvutiteadus. Üldine arvutiteadus
Mathematics. Theoretical computer science
Matemaatika. Teoreetiline arvutiteadus
keyword
deep learning
function evaluation
learning systems
optimization
random forests
ISSN
0302-9743
ISBN
978-303118839-8
notes
Bibliogr.: 38 ref
scientific publication
teaduspublikatsioon
classifier
3.1
TalTech department
Targa linna tippkeskus
language
inglise