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Hidden Markov Models (HMMs) can be accurately approximated using co-occurrence frequencies of pairs and triples of observations by using a fast spectral method in contrast to the usual slow methods like EM or Gibbs sampling.
On the definition of a family of automata
Schutzenbeegeb, MP · 1961
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Probability inequalities for sums of bounded random variables
Hoeffding, Wassily · 1963
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A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains
Baum, L.E., Petrie, T., Soules, G., and Weiss, N · 1970
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Realizations by stochastic finite automata
Carlyle, J.W. and Paz, A · 1971
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Matrices de hankel
Fliess, M · 1974
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Maximum likelihood from incomplete data via the em algorithm
Dempster, A.P., Laird, N.M., and Rubin, D.B · 1977
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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
Geman, Stuart and Geman, Donald · 1984
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A tutorial on hidden markov models and selected applications in speech recognition
Rabiner, L.R · 1989
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Observable operator models for discrete stochastic time series
Jaeger, Herbert · 2000
Cited alongside, same era.
Predictive representations of state
Littman, M.L., Sutton, R.S., and Singh, S · 2002
Cited alongside, same era.
On the learnability of hidden markov models
Terwijn, S · 2002
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A spectral algorithm for learning hidden markov models
Hsu, Daniel, Kakade, Sham M., and Zhang, Tong · 2009
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Reduced-rank hidden markov models
Siddiqi, S.M., Boots, B., and Gordon, G.J · 2009
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Hilbert space embeddings of hidden markov models
Boots, B., Siddiqi, S.M., Gordon, G., and Smola, A · 2010
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An online spectral learning algorithm for partially observable nonlinear dynamical systems
Boots, B. and Gordon, G.J · 2011
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