Correntropy-based metric for robust twin support vector machine

DOI10.1016/j.ins.2020.07.068zbMath1475.68307OpenAlexW3046973977MaRDI QIDQ2054026

Yanyan Li

Publication date: 30 November 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.07.068

zbMATH Keywords

robustnessDC programmingmetric learningtwin support vector machinecorrentropy

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Convex programming (90C25) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

MBSVR: multiple birth support vector regression ⋮ R-CTSVM+: robust capped \(\mathrm{L}_1\)-norm twin support vector machine with privileged information ⋮ Capped \(L_{2,p}\)-norm metric based robust least squares twin support vector machine for pattern classification ⋮ Sparse and robust estimation with ridge minimax concave penalty

Uses Software

• UCI-ml

Cites Work

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• A unified approach to model selection and sparse recovery using regularized least squares
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• Robust capped L1-norm twin support vector machine
• Recursive projection twin support vector machine via within-class variance minimization
• Should we really use post-hoc tests based on mean-ranks?
• Correntropy: Properties and Applications in Non-Gaussian Signal Processing
• Generalized correlation function: definition, properties, and application to blind equalization
• Foundations of Support Constraint Machines
• On Estimation of a Probability Density Function and Mode
• Robust Statistics

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