An improvement on parametric \(\nu\)-support vector algorithm for classification

DOI10.1007/s10479-017-2724-8zbMath1493.68303OpenAlexW2777705644MaRDI QIDQ2329896

Panos M. Pardalos, Hossein Moosaei, Saeed Ketabchi, Mohamad Razzaghi

Publication date: 18 October 2019

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10479-017-2724-8

zbMATH Keywords

classificationgeneralized Newton methodsupport vector regression\(\nu\)-support vector machinesparametric \(\nu\)-support vectorparametric margin

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Quadratic programming (90C20)

Related Items (5)

Least squares approach to K-SVCR multi-class classification with its applications ⋮ Newton-based approach to solving K-SVCR and twin-KSVC multi-class classification in the primal space ⋮ A Lagrangian-based approach for universum twin bounded support vector machine with its applications ⋮ Multi-objective optimization of crude oil-supply portfolio based on interval prediction data ⋮ Smooth augmented Lagrangian method for twin bounded support vector machine

Uses Software

• UCI-ml
• SSVM

Cites Work

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• Robust generalized eigenvalue classifier with ellipsoidal uncertainty
• Generalized Hessian matrix and second-order optimality conditions for problems with \(C^{1,1}\) data
• Financial forecasting using support vector machines
• New support vector algorithms with parametric insensitive/margin model
• Minimum norm solution to the absolute value equation in the convex case
• Minimum norm solution to the positive semidefinite linear complementarity problem
• SSVM: A smooth support vector machine for classification

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