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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