Finite rank kernels for multi-task learning
type: Item
instance of: scholarly article
title: Finite rank kernels for multi-task learning
zbMATH Open document ID: 1267.41003
author: Jianqiang Liu
author: Charles A. Micchelli
author: Rui Wang
author: Yuesheng Xu
published in: Advances in Computational Mathematics
publication date: 2013-04-15T00:00:00Z
Mathematics Subject Classification ID: 41A05
Mathematics Subject Classification ID: 41A10
Mathematics Subject Classification ID: 46E22
zbMATH DE Number: 6153904
zbMATH Keywords: multi-task polynomial kernels
zbMATH Keywords: characteristic operator
zbMATH Keywords: Weierstrass approximation theorem
zbMATH Keywords: continuous kernel extension
MaRDI profile type: Publication
full work available at URL: s10444-011-9244-x
OpenAlex ID: W1995431478
cites work: Convex multi-task feature learning
cites work: When is there a representer theorem? Vector versus matrix regularizers
cites work: On spectral learning
cites work: Theory of Reproducing Kernels
cites work: 10.1162/153244304322765658
cites work: Q3096164
cites work: Support-vector networks
cites work: Q3093270
cites work: On Learning Vector-Valued Functions
cites work: Q3174056
cites work: Q4413262
cites work: Q4375383
cites work: Learning rates for regularized classifiers using multivariate polynomial kernels
cites work: Q4261789
DOI: 10.1007/S10444-011-9244-X
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description: scientific article; zbMATH DE number 6153904