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Regularized Variational Bayesian Learning of Echo State Networks with Delay&Sum Readout

Author(s): Shutin, Dmitriy; Zechner, Christoph; Kulkarni, Sanjeev R; Poor, H Vincent

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Abstract: In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational Bayesian ESN training scheme. The variational approach allows for a seamless combination of sparse Bayesian learning ideas and a variational Bayesian space-alternating generalized expectation-maximization (VB-SAGE) algorithm for estimating parameters of superimposed signals. While the former method realizes automatic regularization of ESNs, which also determines which echo states and input signals are relevant for “explaining” the desired signal, the latter method provides a basis for joint estimation of D&S readout parameters. The proposed training algorithm can naturally be extended to ESNs with fixed filter neurons. It also generalizes the recently proposed expectation-maximization-based D&S readout adaptation method. The proposed algorithm was tested on synthetic data prediction tasks as well as on dynamic handwritten character recognition.
Publication Date: Apr-2012
Citation: Shutin, Dmitriy, Zechner, Christoph, Kulkarni, Sanjeev R, Poor, H Vincent. (2012). Regularized Variational Bayesian Learning of Echo State Networks with Delay&Sum Readout. Neural Computation, 24 (4), 967 - 995. doi:10.1162/neco_a_00253
DOI: doi:10.1162/neco_a_00253
ISSN: 0899-7667
EISSN: 1530-888X
Pages: 967 - 995
Language: en
Type of Material: Journal Article
Journal/Proceeding Title: Neural Computation
Version: Author's manuscript

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