An adaptive support vector regression based on a new sequence of unified orthogonal polynomials

DOI10.1016/J.PATCOG.2012.09.001zbMath1254.68235OpenAlexW1993361892MaRDI QIDQ1930445

Jinwei Zhao, Boqin Feng, Wentao Mao, Guirong Yan, Junqing Bai

Publication date: 11 January 2013

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2012.09.001

zbMATH Keywords

Chebyshev polynomialsgeneralization abilitykernel functionsmall sampleadaptable measures

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

Multi-variable regression methods using modified Chebyshev polynomials of class 2 ⋮ A center sliding Bayesian binary classifier adopting orthogonal polynomials ⋮ Structural nonparallel support vector machine for pattern recognition ⋮ New Hermite orthogonal polynomial kernel and combined kernels in support vector machine classifier ⋮ Maximum likelihood optimal and robust support vector regression with \textit{lncosh} loss function

Uses Software

• SimpleMKL
• SVM

This page was built for publication: An adaptive support vector regression based on a new sequence of unified orthogonal polynomials