Zobrazeno 1 - 10
of 108
pro vyhledávání: '"nonlinear loss functions"'
Autor:
Aykut Kocaoğlu
Publikováno v:
Applied Sciences, Vol 14, Iss 9, p 3641 (2024)
While traditional support vector regression (SVR) models rely on loss functions tailored to specific noise distributions, this research explores an alternative approach: ε-ln SVR, which uses a loss function based on the natural logarithm of the hype
Externí odkaz:
https://doaj.org/article/fdb929820b494aaba7b37c4184342233
Akademický článek
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Autor:
Ghahtarani, Alireza1 (AUTHOR) alireza.ghahtarani@dal.ca, Saif, Ahmed1 (AUTHOR) ahmed.saif@dal.ca, Ghasemi, Alireza1 (AUTHOR) alireza.ghasemi@dal.ca
Publikováno v:
European Journal of Operational Research. Oct2024, Vol. 318 Issue 2, p500-519. 20p.
Autor:
Schild, Albert1,2, Fredman, Irwin J.1
Publikováno v:
Management Science. Oct62, Vol. 9 Issue 1, p73-81. 9p.
Autor:
Devraj, Adithya M., Chen, Jianshu
We consider a generic empirical composition optimization problem, where there are empirical averages present both outside and inside nonlinear loss functions. Such a problem is of interest in various machine learning applications, and cannot be direc
Externí odkaz:
http://arxiv.org/abs/1907.09150
Publikováno v:
Electronics (2079-9292); Dec2024, Vol. 13 Issue 23, p4582, 18p
Autor:
Haderlein, Jonas F., Peterson, Andre D. H., Eskikand, Parvin Zarei, Burkitt, Anthony N., Mareels, Iven M. Y., Grayden, David B.
The empirical success of machine learning models with many more parameters than measurements has generated an interest in the theory of overparameterisation, i.e., underdetermined models. This paradigm has recently been studied in domains such as dee
Externí odkaz:
http://arxiv.org/abs/2304.08066
Publikováno v:
IEEE Transactions on Vehicular Technology; Mar2018, Vol. 67 Issue 3, p1881-1893, 13p
Publikováno v:
Lithuanian Mathematical Journal; Jul2024, Vol. 64 Issue 3, p255-266, 12p
Language-supervised vision models have recently attracted great attention in computer vision. A common approach to build such models is to use contrastive learning on paired data across the two modalities, as exemplified by Contrastive Language-Image
Externí odkaz:
http://arxiv.org/abs/2302.06232