Zobrazeno 1 - 10
of 234
pro vyhledávání: '"Kumari, Sushma"'
Autor:
Kumari, Sushma, Pestov, Vladimir G.
Publikováno v:
ESAIM Probability & Statistics 28(2024), 132-160
We continue to investigate the $k$ nearest neighbour ($k$-NN) learning rule in complete separable metric spaces. Thanks to the results of C\'erou and Guyader (2006) and Preiss (1983), this rule is known to be universally consistent in every such metr
Externí odkaz:
http://arxiv.org/abs/2305.17282
Publikováno v:
Journal of Business & Industrial Marketing, 2023, Vol. 39, Issue 3, pp. 553-567.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JBIM-06-2023-0361
Autor:
Tiwari, Manisha, Bryde, David J., Stavropoulou, Foteini, Dubey, Rameshwar, Kumari, Sushma, Foropon, Cyril
Publikováno v:
In Transportation Research Part E August 2024 188
Publikováno v:
In Materials Science & Engineering B August 2024 306
Autor:
Khan, Nausad, Mahajan, Anima, Arora, Arushi, Sood, Kritika, Kumari, Sushma, Ghosh, Santanu, Jha, Menaka
Publikováno v:
In Materials Chemistry and Physics 1 July 2024 320
Publikováno v:
International Journal of Production Research; Apr2024, Vol. 62 Issue 8, p3044-3058, 15p
Autor:
Kumari, Sushma
Much research has been done for debunking and analysing fake news. Many researchers study fake news detection in the last year, but many are limited to social media data. Currently, multiples fact-checkers are publishing their results in various form
Externí odkaz:
http://arxiv.org/abs/2108.05419
Publikováno v:
In Materials Chemistry and Physics 15 February 2024 314
Autor:
Khurana Sarbjeet, Sadija Jagdish, Kumari Sushma, Gotewal Sangeeta, Sinha Uday Kumar, Singh Ravinder, Dhamija Rajinder Kumar
Publikováno v:
Indian Journal of Community Medicine, Vol 49, Iss 7, Pp 102-102 (2024)
Background: Employee mental health has a significant impact on productivity at work. People who work in healthcare environments are known to be at an increased risk of developing mental health issues. Objective: To assess the psychological distress i
Externí odkaz:
https://doaj.org/article/12e22c42b1884b9a89f5681f68e5cdfe
Publikováno v:
ESAIM: Probability and Statistics 24 (2020), 914--934
The $k$ nearest neighbour learning rule (under the uniform distance tie breaking) is universally consistent in every metric space $X$ that is sigma-finite dimensional in the sense of Nagata. This was pointed out by C\'erou and Guyader (2006) as a con
Externí odkaz:
http://arxiv.org/abs/2003.00894