Cargando…
Kernel-Free Quadratic Surface Support Vector Regression with Non-Negative Constraints
In this paper, a kernel-free quadratic surface support vector regression with non-negative constraints (NQSSVR) is proposed for the regression problem. The task of the NQSSVR is to find a quadratic function as a regression function. By utilizing the quadratic surface kernel-free technique, the model...
Autores principales: | Wei, Dong, Yang, Zhixia, Ye, Junyou, Yang, Xue |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378113/ https://www.ncbi.nlm.nih.gov/pubmed/37509977 http://dx.doi.org/10.3390/e25071030 |
Ejemplares similares
-
Kernel-Free Quadratic Surface Regression for Multi-Class Classification
por: Wang, Changlin, et al.
Publicado: (2023) -
Tame kernels and Tate kernels of quadratic number fields
por: Qin, H
Publicado: (1998) -
A range division and contraction approach for nonconvex quadratic program with quadratic constraints
por: Xue, Chunshan, et al.
Publicado: (2016) -
An optimal consumption and investment problem with quadratic utility and negative wealth constraints
por: Roh, Kum-Hwan, et al.
Publicado: (2017) -
Neural kernels for recursive support vector regression as a model for episodic memory
por: Leibold, Christian
Publicado: (2022)