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|Title:||The copula echo state network||Authors:||Chatzis, Sotirios P.
Chatzis, Sotirios P.
|Issue Date:||2012||Publisher:||Elsevier||Source:||Pattern recognition, 2012, Volume 45, Issue 1, Pages 570–577||Abstract:||Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple, computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. This paper studies the formulation of a class of copula-based semiparametric models for sequential data modeling, characterized by nonparametric marginal distributions modeled by postulating suitable echo state networks, and parametric copula functions that help capture all the scale-free temporal dependence of the modeled processes. We provide a simple algorithm for the data-driven estimation of the marginal distribution and the copula parameters of our model under the maximum-likelihood framework. We exhibit the merits of our approach by considering a number of applications; as we show, our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs, without significant compromises in the algorithm's computational efficiency||URI:||http://ktisis.cut.ac.cy/handle/10488/7239||ISSN:||0031-3203||DOI:||10.1016/j.patcog.2011.06.022||Rights:||© 2011 Elsevier Ltd. All rights reserved|
|Appears in Collections:||Άρθρα/Articles|
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