Scaling up twin support vector regression with safe screening rule

DOI10.1016/j.ins.2018.07.008zbMath1450.62081OpenAlexW2872515887WikidataQ129555445 ScholiaQ129555445MaRDI QIDQ2200535

Yitian Xu, Hongmei Wang

Publication date: 22 September 2020

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

Full work available at URL: https://doi.org/10.1016/j.ins.2018.07.008

zbMATH Keywords

variational inequalitylarge-scale problemsparsetwin support vector regressionquadratic programming problems (QPPs)safe screening rule

Mathematics Subject Classification ID

General nonlinear regression (62J02) Quadratic programming (90C20)

Related Items (2)

A safe double screening strategy for elastic net support vector machine ⋮ A safe reinforced feature screening strategy for Lasso based on feasible solutions

Uses Software

• TPMSVM

Cites Work

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• Clipping algorithms for solving the nearest point problem over reduced convex hulls
• TSVR: an efficient twin support vector machine for regression
• Support-vector networks
• TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition
• Safe Feature Elimination in Sparse Supervised Learning
• Foundations of Optimization

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