Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7234
Title: A reservoir-driven non-stationary hidden markov model
Authors: Demiris, Yiannis 
Chatzis, Sotirios P. 
Demiris, Yiannis 
Keywords: Pattern recognition
Computer science
Markov processes
Issue Date: 2012
Publisher: Elsevier
Source: Pattern recognition, 2012, Volume 45, Issue 11, Pages 3985–3996
Abstract: In this work, we propose a novel approach towards sequential data modeling that leverages the strengths of hidden Markov models and echo-state networks (ESNs) in the context of non-parametric Bayesian inference approaches. We introduce a non-stationary hidden Markov model, the time-dependent state transition probabilities of which are driven by a high-dimensional signal that encodes the whole history of the modeled observations, namely the state vector of a postulated observations-driven ESN reservoir. We derive an efficient inference algorithm for our model under the variational Bayesian paradigm, and we examine the efficacy of our approach considering a number of sequential data modeling applications
URI: http://ktisis.cut.ac.cy/handle/10488/7234
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2012.04.018
Rights: © 2012 Elsevier Ltd. All rights reserved
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