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

CORE Recommender
Sorry the service is unavailable at the moment. Please try again later.
Show full item record

SCOPUSTM   
Citations

13
checked on Nov 9, 2023

WEB OF SCIENCETM
Citations 5

10
Last Week
0
Last month
0
checked on Oct 29, 2023

Page view(s)

537
Last Week
0
Last month
5
checked on Jan 31, 2025

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.