Please use this identifier to cite or link to this item:
|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|
Show full item record
checked on May 27, 2017
WEB OF SCIENCETM
checked on Jun 19, 2017
checked on Jun 23, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.