Training sparse least squares support vector machines by the QR decomposition

DOI10.1016/J.NEUNET.2018.07.008zbMath1434.68475DBLPjournals/nn/Xia18OpenAlexW2883397905WikidataQ90741789 ScholiaQ90741789MaRDI QIDQ2182874

Xiao-Lei Xia

Publication date: 26 May 2020

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

Full work available at URL: https://pure.qub.ac.uk/en/publications/training-sparse-least-squares-support-vector-machines-by-the-qr-decomposition(14cdb536-a079-4a29-9d79-d7384a57ea8b).html

zbMATH Keywords

QR decompositionsparsenessleast-squares support vector machineskernel matching pursuit

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• LIBSVM

Cites Work

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• A novel sparse least squares support vector machines
• Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
• Weighted least squares support vector machines: robustness and sparse approximation
• SMO Algorithm for Least-Squares SVM Formulations
• Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
• Kernel matching pursuit

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