Artículo
Nonstationary regression with support vector machines
Fecha de publicación:
07/10/2014
Editorial:
Springer
Revista:
Neural Computing And Applications
ISSN:
0941-0643
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
In this work, we introduce a method for data analysis in nonstationary environments: time-adaptive support vector regression (TA-SVR). The proposed approach extends a previous development that was limited to classification problems. Focusing our study on time series applications, we show that TA-SVR can improve the accuracy of several aspects of nonstationary data analysis, namely the tasks of modelling and prediction, input relevance estimation, and reconstruction of a hidden forcing profile.
Palabras clave:
Regression
,
Support Vector Machine
,
Nonstationary Problems
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Articulos(CIFASIS)
Articulos de CENTRO INT.FRANCO ARG.D/CS D/L/INF.Y SISTEM.
Articulos de CENTRO INT.FRANCO ARG.D/CS D/L/INF.Y SISTEM.
Citación
Uzal, Lucas César; Grinblat, Guillermo Luis; Granitto, Pablo Miguel; Verdes, Pablo Fabian; Nonstationary regression with support vector machines; Springer; Neural Computing And Applications; 26; 3; 7-10-2014; 641-649
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