Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1727
Title: The Copula Echo State Network
Authors: Chatzis, Sotirios P. 
Demiris, Yiannis 
metadata.dc.contributor.other: Χατζής, Σωτήριος Π.
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Pattern Recognition;Computer science;Algorithms;Benchmarking;Neural networks
Issue Date: Jan-2012
Source: Pattern recognition, 2012, vol. 45, no. 1, pp. 570–577
Volume: 45
Issue: 1
Start page: 570
End page: 577
Journal: Pattern Recognition 
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: https://hdl.handle.net/20.500.14279/1727
ISSN: 00313203
DOI: 10.1016/j.patcog.2011.06.022
Rights: © 2011 Elsevier Ltd. All rights reserved.
Type: Article
Affiliation : Imperial College London 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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