R. Agrawal, C. Faloutsos, and A. N. Swami, Efficient similarity search in sequence databases, Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms, FODO '93, pp.69-84, 1993.

R. M. Altman-and-a and . Petkau, Application of hidden markov models to multiple sclerosis lesion count data, Statistics in Medicine, vol.24, pp.2335-2344, 2005.

A. and S. I. Marcus, Analysis of an identification algorithm arising in the adaptive estimation of markov chains, Mathematics of Control, Signals and Systems, vol.3, pp.1-29, 1990.

P. Baldi and Y. Chauvin, Smooth on-line learning algorithms for hidden markov models, Neural Comput, vol.6, pp.307-318, 1994.

L. E. Baum, T. Petrie, G. Soules, and A. N. Weiss, A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains, The Annals of Mathematical Statistics, vol.41, pp.164-171, 1970.

C. Biernacki, G. Celeux, and A. G. Govaert, Choosing starting values for the {EM} algorithm for getting the highest likelihood in multivariate gaussian mixture models, Computational Statistics & Data Analysis, pp.561-575, 2003.

O. Cappe, V. Buchoux, and A. E. Moulines, Quasinewton method for maximum likelihood estimation of hidden markov models, Proceedings of the 1998 IEEE International Conference on, vol.4, pp.2265-2268, 1998.

G. Celeux, D. Chauveau, and A. J. Diebolt, On Stochastic Versions of the EM Algorithm, 1995.
URL : https://hal.archives-ouvertes.fr/inria-00074164

G. Celeux-and-g and . Govaert, A classification {EM} algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, pp.315-332, 1992.

K. Fu, Efficient time series matching by wavelets, in Data Engineering, Proceedings., 15th International Conference on, pp.126-133, 1999.

I. B. Collings, V. Krishnamurthy, and J. B. Moore, On-line identification of hidden markov models via recursive prediction error techniques, IEEE Transactions on Signal Processing, vol.42, pp.3535-3539, 1994.

. Datamarket!, The open portal to thousands of datasets from leading global providers, 2013.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society, series B, vol.39, pp.1-38, 1977.

R. Durbin, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, 1998.

J. Fessler-and-a and . Hero, Space-alternating generalized expectation-maximization algorithm, Signal Processing, IEEE Transactions on, vol.42, pp.2664-2677, 1994.

G. Florez-larrahondo, S. E. Bridges, and . Hansen, Incremental estimation of discrete hidden markov models based on a new backward procedure, Proceedings of the 20th National Conference on Artificial Intelligence, vol.2, pp.758-763, 2005.

A. K. Garg-and-m and . Warmuth, Inline updates for hmms, Interspeech, ISCA, 2003.

M. I. Jamshidian-and-r and . Jennrich, Conjugate gradient acceleration of the em algorithm, Journal of the American Statistical Association, vol.88, pp.221-228, 1993.

F. Korn, H. V. Jagadish, and A. C. Faloutsos, Efficiently supporting ad hoc queries in large datasets of time sequences, Proceedings of the 1997 ACM SIGMOD international conference on Management of data, vol.97, pp.289-300, 1997.

V. Krishnamurthy and J. B. Moore, On-line estimation of hidden markov model parameters based on the kullbackleibler information measure, IEEE Transactions on Signal Processing, vol.41, pp.2557-2573, 1993.

F. Legland-and-l and . Mevel, Recursive identification of hmms with observations in a finite set, in Decision and Control, Proceedings of the 34th IEEE Conference on, vol.1, pp.216-221, 1995.

J. Lin, E. Keogh, S. Lonardi, and A. B. Chiu, A symbolic representation of time series, with implications for streaming algorithms, Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, DMKD '03, pp.2-11, 2003.

C. B. Liu-and-d and . Rubin, The ecme algorithm: A simple extension of em and ecm with faster monotone convergence, Biometrika, vol.81, pp.633-648, 1994.

C. Liu, D. B. Rubin, and Y. N. Wu, Parameter expansion to accelerate em: The px-em algorithm, Biometrika, vol.85, pp.755-770, 1998.

R. Salakhutdinov, S. Roweis, and A. Z. Ghahramani, Expectation-Conjugate Gradient: An Alternative to EM

S. S. Sivaprakasam-and-k and . Shanmugan, A forwardonly recursion based hmm for modeling burst errors in digital channels, Global Telecommunications Conference, 1995. GLOBECOM '95, vol.2, pp.1054-1058, 1995.

M. A. Tanner-and-w and . Wong, The calculation of posterior distributions by data augmentation, Journal of the American Statistical Association, vol.82, pp.528-540, 1987.

R. Turner, Direct maximization of the likelihood of a hidden markov model, Computational Statistics & Data Analysis, vol.52, pp.4147-4160, 2008.

D. A. Van-dyk, X. Meng, and D. B. Rubin, Maximum likelihood estimation via the ecm algorithm: computing the asymptotic variance, Statistica Sinica, pp.55-75, 1995.

G. C. Wei-and-m and . Tanner, A monte carlo implementation of the em algorithm and the poor man's data augmentation algorithms, Journal of the American Statistical Association, vol.85, pp.699-704, 1990.

W. A. Zucchini and . Macdonald, Hidden Markov Models for Time Series: An Introduction Using R, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, 2009.