Titre
Time Series Input Selection using Multiple Kernel Learning
Type
article de conférence/colloque
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Auteur(s)
Foresti, L.
Auteure/Auteur
Tuia, D.
Auteure/Auteur
Timonin, V.
Auteure/Auteur
Kanevski, M.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
Titre du livre ou conférence/colloque
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
Première page
123
Dernière page/numéro d’article
128
Peer-reviewed
Oui
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).
PID Serval
serval:BIB_6B6AADC917C2
Date de création
2013-11-25T16:18:19.018Z
Date de création dans IRIS
2025-05-21T03:04:41Z