TY - GEN
T1 - Recursive maximum likelihood estimation for hidden semi-Markov models
AU - Squire, Kevin
AU - Levinson, Stephen E.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - The term hidden semi-Markov model (HSMM) refers to a large class of stochastic models developed to address some of the shortcomings of hidden Markov models (HMMs). As with HMMs, the underlying sequence of states of a process is modelled as a discrete Markov chain. Unlike HMMs, each state in an HSMM can emit a variable length sequence of observations, with many ways to model duration and observation densities. Parameter estimation in HSMMs is typically done using EM or Viterbi (dynamic programming) algorithms. These algorithms require batch processing of large amounts of data, and so are not useful for online learning. To address this issue, we present here a recursive maximum-likelihood estimation (RMLE) algorithm for online estimation of HSMM parameters, based on a similar method developed for HMMs.
AB - The term hidden semi-Markov model (HSMM) refers to a large class of stochastic models developed to address some of the shortcomings of hidden Markov models (HMMs). As with HMMs, the underlying sequence of states of a process is modelled as a discrete Markov chain. Unlike HMMs, each state in an HSMM can emit a variable length sequence of observations, with many ways to model duration and observation densities. Parameter estimation in HSMMs is typically done using EM or Viterbi (dynamic programming) algorithms. These algorithms require batch processing of large amounts of data, and so are not useful for online learning. To address this issue, we present here a recursive maximum-likelihood estimation (RMLE) algorithm for online estimation of HSMM parameters, based on a similar method developed for HMMs.
UR - http://www.scopus.com/inward/record.url?scp=33749061970&partnerID=8YFLogxK
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U2 - 10.1109/MLSP.2005.1532923
DO - 10.1109/MLSP.2005.1532923
M3 - Conference contribution
AN - SCOPUS:33749061970
SN - 0780395174
SN - 9780780395176
T3 - 2005 IEEE Workshop on Machine Learning for Signal Processing
SP - 329
EP - 334
BT - 2005 IEEE Workshop on Machine Learning for Signal Processing
T2 - 2005 IEEE Workshop on Machine Learning for Signal Processing
Y2 - 28 September 2005 through 30 September 2005
ER -