Ideal regularization for learning kernels from labels
type: Item
instance of: scholarly article
title: Ideal regularization for learning kernels from labels
zbMATH Open document ID: 1325.68194
author: Binbin Pan
author: James Lai
author: Lixin Shen
published in: Neural Networks
publication date: 2015-11-06T00:00:00Z
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6505376
zbMATH Keywords: kernel methods
zbMATH Keywords: regularization
zbMATH Keywords: labels
zbMATH Keywords: ideal kernel
zbMATH Keywords: semi-supervised learning
zbMATH Keywords: von Neumann divergence
describes a project that uses: SHOGUN
describes a project that uses: YALMIP
describes a project that uses: SimpleMKL
describes a project that uses: SVMlight
MaRDI profile type: Publication
full work available at URL: j.neunet.2014.04.003
OpenAlex ID: W2018967050
Wikidata QID: Q45790669
cites work: Q3174044
cites work: Q4209222
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cites work: Algorithmic Learning Theory
cites work: Q5405129
cites work: Q3093181
cites work: 10.1162/153244302760200687
cites work: Nonlinear nonnegative matrix factorization based on Mercer kernel construction
cites work: Q3096204
cites work: Learning Theory and Kernel Machines
cites work: Q3093403
cites work: Learning the kernel matrix by maximizing a KFD-based class separability criterion
cites work: Q5396642
DOI: 10.1016/J.NEUNET.2014.04.003
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description: scientific article; zbMATH DE number 6505376