Fast and strong convergence of online learning algorithms

DOI10.1007/s10444-019-09707-8zbMath1433.68344arXiv1710.03600OpenAlexW2964017994WikidataQ127743100 ScholiaQ127743100MaRDI QIDQ2305549

Lei Shi, Zheng-Chu Guo

Publication date: 11 March 2020

Published in: Advances in Computational Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1710.03600

zbMATH Keywords

learning theoryonline learningcapacity dependent error analysisstrong convergence in an RKHS

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) Online algorithms; streaming algorithms (68W27)

Related Items (6)

Online gradient descent algorithms for functional data learning ⋮ Distributed spectral pairwise ranking algorithms ⋮ Rates of convergence of randomized Kaczmarz algorithms in Hilbert spaces ⋮ Capacity dependent analysis for functional online learning algorithms ⋮ Online regularized learning algorithm for functional data ⋮ Convergence analysis of online learning algorithm with two-stage step size

Uses Software

• gss
• Pegasos

Cites Work

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