Gradient learning in a classification setting by gradient descent

DOI10.1016/j.jat.2008.12.002zbMath1187.68381OpenAlexW2064904501MaRDI QIDQ1048984

Jia Cai, Ding-Xuan Zhou, Hong-Yan Wang

Publication date: 8 January 2010

Published in: Journal of Approximation Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jat.2008.12.002

zbMATH Keywords

learning theoryreproducing kernel Hilbert spaceapproximation errorgradient descentclassification algorithm with convex loss

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22)

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Complex-order particle swarm optimization ⋮ Approximation analysis of gradient descent algorithm for bipartite ranking

Cites Work

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• The covering number in learning theory
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• Online learning algorithms
• Shannon sampling. II: Connections to learning theory
• Learning theory estimates via integral operators and their approximations
• Capacity of reproducing kernel spaces in learning theory
• Online Regularized Classification Algorithms
• Shannon sampling and function reconstruction from point values
• Convexity, Classification, and Risk Bounds

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