On the sparseness of 1-norm support vector machines

DOI10.1016/j.neunet.2009.11.012zbMath1396.68109OpenAlexW1993105429WikidataQ37657911 ScholiaQ37657911MaRDI QIDQ1784565

Li Zhang, Wei-Da Zhou

Publication date: 27 September 2018

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2009.11.012

zbMATH Keywords

sparse learningsparseness1-norm SVMstandard SVM

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (6)

Sparse ensembles using weighted combination methods based on linear programming ⋮ A semismooth Newton method for support vector classification and regression ⋮ Analysis of programming properties and the row-column generation method for 1-norm support vector machines ⋮ 1-norm support vector novelty detection and its sparseness ⋮ Tropical Support Vector Machines: Evaluations and Extension to Function Spaces ⋮ Support vector machine classifiers by non-Euclidean margins

Uses Software

• UCI-ml
• LIBSVM

Cites Work

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• Matching pursuits with time-frequency dictionaries
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• OnL₁-Norm Multiclass Support Vector Machines
• Stable signal recovery from incomplete and inaccurate measurements
• Compressed sensing
• Kernel matching pursuit
• Linear programming boosting via column generation
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