Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Yogendra Meena"'
Autor:
Swati Sharma, Ritika Sinha, Anil K. Singh, Yogendra Meena, Alemwati Pongener, Rohit Sharma, Tusar Kanti Behera, Kalyan Barman
Publikováno v:
Food Chemistry Advances, Vol 3, Iss , Pp 100433- (2023)
Trichosanthes dioica Roxb. is a widely cultivated cucurbitaceous vegetable of tropical and sub-tropical regions. Its unripe fruits are consumed as vegetable. This review aims to present an umbrella overview of botany, ethnomedicinal uses, nutritional
Externí odkaz:
https://doaj.org/article/2e5f2cf07f7047498b91a57bd61f5a57
Publikováno v:
International Journal of Current Microbiology and Applied Sciences. 10:2821-2826
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2115bb33456b7fde5dbc981b478267c
https://doi.org/10.20546/ijcmas.2021.1002.313
https://doi.org/10.20546/ijcmas.2021.1002.313
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 35:5231-5239
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05d1ee85f739dd737e275ee8395c4cd2
https://doi.org/10.3233/jifs-169807
https://doi.org/10.3233/jifs-169807
Autor:
Yogendra Meena, S. Balasundaram
Publikováno v:
Neural Processing Letters. 49:1399-1431
As real world data sets in general contain noise, construction of robust regression learning models to fit data with noise is an important and challenging research problem. It is all the more difficult to learn regression function with good generaliz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d77f4dbe1064344138231542799008de
https://doi.org/10.1007/s11063-018-9875-8
https://doi.org/10.1007/s11063-018-9875-8
Publikováno v:
Proceedings of ICETIT 2019 ISBN: 9783030305765
Support vector regression (SVR) method becomes the state of the art machine learning method for data regression due to its excellent generalization performance on many real-world problems. It is well-known that the standard SVR determines the regress
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f87dfec82b749eceadf2763b54b31e6
https://doi.org/10.1007/978-3-030-30577-2_16
https://doi.org/10.1007/978-3-030-30577-2_16
Autor:
S. Balasundaram, Yogendra Meena
Publikováno v:
Knowledge and Information Systems. 49:1097-1129
In this paper, a novel root finding problem for the Lagrangian support vector regression in 2-norm (LSVR) is formulated in which the number of unknowns becomes the number of training examples. Further, it is proposed to solve it by functional iterati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49b70e55e516cbe174b07dead080fb11
https://doi.org/10.1007/s10115-016-0928-x
https://doi.org/10.1007/s10115-016-0928-x
Autor:
Yogendra Meena, S. Balasundaram
Publikováno v:
Applied Intelligence. 44:931-955
In this paper, we propose a new unconstrained twin support vector regression model in the primal space (UPTSVR). With the addition of a regularization term in the formulation of the problem, the structural risk is minimized. The proposed formulation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2aedee6b72bf6929cc9a623c9a8a7928
https://doi.org/10.1007/s10489-015-0731-5
https://doi.org/10.1007/s10489-015-0731-5