In this section
@article{Chatzis:2012,
author = {Chatzis, SP and Demiris, Y},
journal = {Pattern Recognition},
pages = {3985--3996},
title = {A Reservoir-Driven Non-Stationary Hidden Markov Model},
url = {http://hdl.handle.net/10044/1/12611},
volume = {45},
year = {2012}
}
TY - JOUR
AB - 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.
AU - Chatzis,SP
AU - Demiris,Y
EP - 3996
PY - 2012///
SP - 3985
TI - A Reservoir-Driven Non-Stationary Hidden Markov Model
T2 - Pattern Recognition
UR - http://hdl.handle.net/10044/1/12611
VL - 45
ER -
