Fast rates by transferring from auxiliary hypotheses

DOI10.1007/S10994-016-5594-4zbMath1453.68153arXiv1412.1619OpenAlexW2509517678MaRDI QIDQ2361574

Ilja Kuzborskij, Francesco Orabona

Publication date: 30 June 2017

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1412.1619

zbMATH Keywords

Rademacher complexitytransfer learningdomain adaptationfast-rate generalization boundssmooth loss functionsstrongly-convex regularizers

Mathematics Subject Classification ID

General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Handling concept drift via model reuse ⋮ Unnamed Item ⋮ Risk bound of transfer learning using parametric feature mapping and its application to sparse coding

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