Low rank updated LS-SVM classifiers for fast variable selection

DOI10.1016/j.neunet.2007.12.053zbMath1254.68218OpenAlexW2023036152WikidataQ47844022 ScholiaQ47844022MaRDI QIDQ1932006

Bart De Moor, Fabian Ojeda, Johan A. K. Suykens

Publication date: 17 January 2013

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2007.12.053

zbMATH Keywords

variable selectionleast-squares support vector machinesleave-one-out errorlow rank matrix modifications

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

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Uses Software

• LINPACK

Cites Work

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• Benchmarking least squares support vector machine classifiers
• Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
• Wrappers for feature subset selection
• 10.1162/15324430260185619
• Chaos control using least-squares support vector machines
• Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
• Selection bias in gene extraction on the basis of microarray gene-expression data
• Methods for Modifying Matrix Factorizations
• Soft margins for AdaBoost
• Gene selection for cancer classification using support vector machines