\(L_{1}\)-norm loss based twin support vector machine for data recognition

DOI10.1016/j.ins.2016.01.023zbMath1395.68238OpenAlexW2232821992MaRDI QIDQ1671708

Lingyan Kong, Dong Xu, Dongjing Chen, Xin-Jun Peng

Publication date: 7 September 2018

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

Full work available at URL: https://doi.org/10.1016/j.ins.2016.01.023

zbMATH Keywords

matrix inversionsupport vector machinenonparallel hyperplanesgeometric interpretation\(L_{1}\)-norm loss

Mathematics Subject Classification ID

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

Related Items (5)

Fast clustering-based weighted twin support vector regression ⋮ Robust projection twin support vector machine via DC programming ⋮ R-CTSVM+: robust capped \(\mathrm{L}_1\)-norm twin support vector machine with privileged information ⋮ Robust capped L1-norm projection twin support vector machine ⋮ \(L_1\)-norm loss-based projection twin support vector machine for binary classification

Uses Software

• NPPC
• TPMSVM

Cites Work

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• Structural regularized projection twin support vector machine for data classification
• Support vector machine with manifold regularization and partially labeling privacy protection
• Clipping algorithms for solving the nearest point problem over reduced convex hulls
• Nonparallel plane proximal classifier
• Newton's method for nonparallel plane proximal classifier with unity norm hyperplanes
• New support vector algorithms with parametric insensitive/margin model
• Ten lectures on statistical and structural pattern recognition.
• Recursive projection twin support vector machine via within-class variance minimization
• TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition
• A general soft method for learning SVM classifiers with \(L_{1}\)-norm penalty
• A twin-hypersphere support vector machine classifier and the fast learning algorithm
• A \(\nu \)-twin support vector machine (\(\nu \)-TSVM) classifier and its geometric algorithms
• Finding the Point of a Polyhedron Closest to the Origin
• An Iterative Procedure for Computing the Minimum of a Quadratic Form on a Convex Set

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