Parsimonious kernel extreme learning machine in primal via Cholesky factorization

DOI10.1016/j.neunet.2016.04.009zbMath1414.68091OpenAlexW2345318147WikidataQ39749747 ScholiaQ39749747MaRDI QIDQ2418199

Yong-Ping Zhao

Publication date: 3 June 2019

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2016.04.009

zbMATH Keywords

kernel methodextreme learning machineconstructive algorithmparsimoniousnessdestructive algorithm

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• OP-ELM
• RSVM

Cites Work

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• Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression
• Trends in extreme learning machines: a review
• Recursive reduced least squares support vector regression
• Support-vector networks
• Robust support vector regression in the primal
• An unbiased LSSVM model for classification and regression
• 10.1162/jmlr.2003.3.4-5.781
• Training a Support Vector Machine in the Primal
• Learning representations by back-propagating errors

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