new {H}MM e-survey

[Arun Jagota]

New refereed e-publication 
action editor: Yoram Singer

Y. Bengio, Markovian Models for Sequential Data,
Neural Computing Surveys 2, 129--162, 1999. 141 references.

http://www.icsi.berkeley.edu/~jagota/NCS

Abstract:

Hidden Markov Models (HMMs) are statistical models of sequential data
that have been used successfully in many machine learning applications,
especially for speech recognition. Furthermore, in the last few years,
many new and promising probabilistic models related to HMMs have been
proposed.  We first summarize the basics of HMMs, and then review several
recent related learning algorithms and extensions of HMMs, including in
particular hybrids of HMMs with artificial neural networks, Input-Output
HMMs (which are conditional HMMs using neural networks to compute
probabilities), weighted transducers, variable-length Markov models and
Markov switching state-space models. Finally, we discuss some of the
challenges of future research in this very active area.