Time Series Input Selection using Multiple Kernel Learning
Détails
ID Serval
serval:BIB_6B6AADC917C2
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Time Series Input Selection using Multiple Kernel Learning
Titre de la conférence
European symposium on artificial neural network ESANN, Computational Intelligence and Machine Learning, Bruges, Belgium
ISBN
2-930307-10-2
Statut éditorial
Publié
Date de publication
2010
Peer-reviewed
Oui
Pages
123-128
Langue
anglais
Résumé
In this paper we study the relevance of multiple kernel learning (MKL)
for the automatic selection of time series inputs. Recently, MKL
has gained great attention in the machine learning community due
to its flexibility in modelling complex patterns and performing feature
selection. In general, MKL constructs the kernel as a weighted linear
combination of basis kernels, exploiting different sources of information.
An efficient algorithm wrapping a Support Vector Regression model
for optimizing the MKL weights, named SimpleMKL, is used for the
analysis. In this sense, MKL performs feature selection by discarding
inputs/kernels with low or null weights. The approach proposed is
tested with simulated linear and nonlinear time series (AutoRegressive,
Henon and Lorenz series).
for the automatic selection of time series inputs. Recently, MKL
has gained great attention in the machine learning community due
to its flexibility in modelling complex patterns and performing feature
selection. In general, MKL constructs the kernel as a weighted linear
combination of basis kernels, exploiting different sources of information.
An efficient algorithm wrapping a Support Vector Regression model
for optimizing the MKL weights, named SimpleMKL, is used for the
analysis. In this sense, MKL performs feature selection by discarding
inputs/kernels with low or null weights. The approach proposed is
tested with simulated linear and nonlinear time series (AutoRegressive,
Henon and Lorenz series).
Création de la notice
25/11/2013 17:18
Dernière modification de la notice
20/08/2019 14:25
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