@InProceedings{Supelec828,
author = {Lucie Daubigney and Matthieu Geist and Olivier Pietquin},
title = {Random Projections: a Remedy for Overfitting Issues in Time Series Prediction with Echo State Networks},
year = {2013},
booktitle = {Proceedings of the 38th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)},
note = {to appear},
address = {Vancouver, Canada},
url = {http://www.metz.supelec.fr//metz/personnel/geist_mat/pdfs/supelec828.pdf},
abstract = {Modelling time series is quite a difficult task. The last recent
years,
reservoir computing approaches have been proven very efficient for
such problems. Indeed, thanks to recurrence in the connections
between
neurons, this approach is a powerful tool to catch and model
time dependencies between samples. Yet, the prediction quality often
depends on the trade-off between the number of neurons in the
reservoir and the amount of training data. Supposedly, the larger the
number of neurons, the richer the reservoir of dynamics. However,
the risk of overfitting problem appears. Conversely, the lower the
number of neurons is, the lower the risk of overfitting problem is
but also the poorer the reservoir of dynamics is. We consider here
the combination of an echo state network with a projection method
to benefit from the advantages of the reservoir computing approach
without needing to pay attention to overfitting problems due to a
lack
of training data.}
}