Learning Rate for Convex Support Tensor Machines.
IEEE Trans Neural Netw Learn Syst
; 32(8): 3755-3760, 2021 Aug.
Article
em En
| MEDLINE
| ID: mdl-32833645
ABSTRACT
Tensors are increasingly encountered in prediction problems. We extend previous results for high-dimensional least-squares convex tensor regression to classification problems with a hinge loss and establish its asymptotic statistical properties. Based on a general convex decomposable penalty, the rate depends on both the intrinsic dimension and the Rademacher complexity of the class of linear functions of tensor predictors.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Neural Netw Learn Syst
Ano de publicação:
2021
Tipo de documento:
Article