Bayesian support vector regression using a unified loss function.
IEEE Trans Neural Netw
; 15(1): 29-44, 2004 Jan.
Article
em En
| MEDLINE
| ID: mdl-15387245
ABSTRACT
In this paper, we use a unified loss function, called the soft insensitive loss function, for Bayesian support vector regression. We follow standard Gaussian processes for regression to set up the Bayesian framework, in which the unified loss function is used in the likelihood evaluation. Under this framework, the maximum a posteriori estimate of the function values corresponds to the solution of an extended support vector regression problem. The overall approach has the merits of support vector regression such as convex quadratic programming and sparsity in solution representation. It also has the advantages of Bayesian methods for model adaptation and error bars of its predictions. Experimental results on simulated and real-world data sets indicate that the approach works well even on large data sets.
Buscar no Google
Base de dados:
MEDLINE
Assunto principal:
Análise de Regressão
/
Teorema de Bayes
Idioma:
En
Ano de publicação:
2004
Tipo de documento:
Article