Analysis of Regression Algorithms with Unbounded Sampling.
Neural Comput
; 32(10): 1980-1997, 2020 10.
Article
em En
| MEDLINE
| ID: mdl-32795236
ABSTRACT
In this letter, we study a class of the regularized regression algorithms when the sampling process is unbounded. By choosing different loss functions, the learning algorithms can include a wide range of commonly used algorithms for regression. Unlike the prior work on theoretical analysis of unbounded sampling, no constraint on the output variables is specified in our setting. By an elegant error analysis, we prove consistency and finite sample bounds on the excess risk of the proposed algorithms under regular conditions.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Neural Comput
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2020
Tipo de documento:
Article