Zobrazeno 1 - 9
of 9
pro vyhledávání: '"97-XX"'
Autor:
Růžička, Michal, Sojka, Petr
In this paper, we investigate mathematical content representations suitable for the automated classification of and the similarity search in STEM documents using standard machine learning algorithms: the Latent Dirichlet Allocation (LDA) and the Late
Externí odkaz:
http://arxiv.org/abs/2110.04040
Autor:
Solovyova, A., Solovyov, I.
We introduce the deep network trained on the MURA dataset from the Stanford University released in 2017. Our system is able to detect bone abnormalities on the radiographs and visualise such zones. We found that our solution has the accuracy comparab
Externí odkaz:
http://arxiv.org/abs/2008.03356
This article describes what can happen when sustained effort and resources are devoted to creating a teacher professional support and development organization that puts teachers' needs first. Over the last ten years, Math for America Los Angeles has
Externí odkaz:
http://arxiv.org/abs/1910.12826
An on-line drilling system, the tutor-web, has been developed and used for teaching mathematics and statistics. The system was used in a basic course in calculus including 182 students. The students were requested to answer quiz questions in the tuto
Externí odkaz:
http://arxiv.org/abs/1310.8236
Autor:
Iftime, Mihaela D.
This paper presents a nonlinear approach to measurements a general framework for dealing with variations of environmental conditions. My method may prove promising to extensions beyond classical physics, economics, and other sciences. I included few
Externí odkaz:
http://arxiv.org/abs/1006.3685
Autor:
Bolondi, Giorgio
Publikováno v:
Atti della Accademia Peloritana dei Pericolanti : Classe di Scienze Fisiche, Matematiche e Naturali, Vol 99, Iss S1, p A4 (2021)
Atti della Accademia Peloritana dei Pericolanti-Classe di Scienze Fisiche, Matematiche e Naturali; Vol 99, SUPPL NO 1 (2021): New Horizons in Teaching Science 2019; A4
Atti della Accademia Peloritana dei Pericolanti. Classe di Scienze Fisiche, Matematiche e Naturali; Vol 99, SUPPL NO 1 (2021): New Horizons in Teaching Science 2019; A4
Atti della Accademia Peloritana dei Pericolanti-Classe di Scienze Fisiche, Matematiche e Naturali; Vol 99, SUPPL NO 1 (2021): New Horizons in Teaching Science 2019; A4
Atti della Accademia Peloritana dei Pericolanti. Classe di Scienze Fisiche, Matematiche e Naturali; Vol 99, SUPPL NO 1 (2021): New Horizons in Teaching Science 2019; A4
Large-scale surveys on Maths and Science learning (such as OCSE-Pisa, IE-TIMMS, TIMMS Advanced and the INVALSI in Italy) have a strong influence on public opinion in all countries and, in a top-down process, on decisions by policy-makers and administ
Autor:
R����i��ka, Michal, Sojka, Petr
In this paper, we investigate mathematical content representations suitable for the automated classification of and the similarity search in STEM documents using standard machine learning algorithms: the Latent Dirichlet Allocation (LDA) and the Late
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ce3143f5f7fea7fbdaf43e61a96e610
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
An on-line drilling system, the tutor-web, has been developed and used for teaching mathematics and statistics. The system was used in a basic course in calculus including 182 students. The students were requested to answer quiz questions in the tuto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14117176cf69d57f39b98df2d6e15aee