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|Title:||A reservoir-driven non-stationary hidden markov model||Authors:||Demiris, Yiannis
Chatzis, Sotirios P.
|Keywords:||Pattern recognition;Computer science;Markov processes||Category:||Electrical Engineering, Electronic Engineering, Information Engineering||Field:||Engineering and Technology||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:||http://dx.doi.org/10.1016/j.patcog.2012.04.018||Rights:||© 2012 Elsevier Ltd. All rights reserved||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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