Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Rafael Savvides"'
Publikováno v:
Statistical Analysis and Data Mining: The ASA Data Science Journal. 16:162-186
Model selection is one of the most central tasks in supervised learning. Validation set methods are the standard way to accomplish this task: models are trained on training data, and the model with the smallest loss on the validation data is selected
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e979611233f21f00c37034069cf0f42
https://doi.org/10.1002/sam.11606
https://doi.org/10.1002/sam.11606
Publikováno v:
Journal of Safety Research. 82:28-37
Introduction: Finnish companies are legally required to insure their employees against occupational acci-dents. Insurance companies are then required to submit information about occupational accidents to the Finnish Workers' Compensation Center (TVK)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d842e7bc551d4acf80a900d29b1930f
https://doi.org/10.1016/j.jsr.2022.04.003
https://doi.org/10.1016/j.jsr.2022.04.003
Autor:
Jarmo Mäkelä, Laila Melkas, Ivan Mammarella, Tuomo Nieminen, Suyog Chandramouli, Rafael Savvides, Kai Puolamäki
Publikováno v:
Biogeosciences. 19:2095-2099
In this note, we argue that the outputs of causal discovery algorithms should not usually be considered end results but rather starting points and hypotheses for further study. The incentive to explore this topic came from a recent study by Krich et
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d23bd59a687e1a3e185896f076f5b7a2
https://doi.org/10.5194/bg-19-2095-2022
https://doi.org/10.5194/bg-19-2095-2022
Autor:
Jarmo Makela, Kai Puolamäki, Laila Melkas, Suyog Halasinamara Chandramouli, Ivan Mammarella, Rafael Savvides, Tuomo Nieminen
This is a comment on "Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach" by Krich et al., Biogeosciences, 17, 1033–1061, 2020, which gives a good introduction to causal discovery, but confines the scope by in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::657182c44d157778f700db2af666beed
https://doi.org/10.5194/bg-2021-231
https://doi.org/10.5194/bg-2021-231
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics. :1-1
A fundamental problem in visual data exploration concerns whether observed patterns are true or merely random noise. This problem is especially pertinent in visual analytics, where the user is presented with a barrage of patterns, without any guarant
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b15dda92d7395f75679ecc66d471abe
https://doi.org/10.1109/tvcg.2022.3175532
https://doi.org/10.1109/tvcg.2022.3175532
Publikováno v:
KDD
In this paper we consider the following important problem: when we explore data visually and observe patterns, how can we determine their statistical significance? Patterns observed in exploratory analysis are traditionally met with scepticism, since
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b049847b434e6586784d4dfdf6aebe9d
https://doi.org/10.1145/3292500.3330994
https://doi.org/10.1145/3292500.3330994
Autor:
Mona Kurppa, Moritz Johannes Lange, Henri Johannes Suominen, Emilia Oikarinen, Rafael Savvides, Leena Järvi, Kai Puolamäki
Publikováno v:
University of Helsinki
Non
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::6c877ae318e86b115eaa687ef7ae48a3
https://researchportal.helsinki.fi/en/publications/db078b99-746b-4948-9e87-ce99fd4fe1ef
https://researchportal.helsinki.fi/en/publications/db078b99-746b-4948-9e87-ce99fd4fe1ef