Time Series Input Selection using Multiple Kernel Learning


Autoria(s): Foresti L.; Tuia D.; Timonin V.; Kanevski M.
Data(s)

2010

Resumo

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).

Identificador

http://serval.unil.ch/?id=serval:BIB_6B6AADC917C2

isbn:2-930307-10-2

Idioma(s)

en

Fonte

European symposium on artificial neural network ESANN, Computational Intelligence and Machine Learning, Bruges, Belgium

Tipo

info:eu-repo/semantics/conferenceObject

inproceedings