Hidden Markov Models


Important papers in HMMs

  • Lawrence R. Rabiner (February 1989). A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE 77 (2): 257–28[PDF]
  • Airoldi EM (2007) Getting Started in Probabilistic Graphical Models. PLoS Comput Biol 3(12): e252.[PDF]
  • B. H. Juang, L. R. Rabiner. The segmental K-means algorithm for estimating parameters of hidden Markov models. IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 38, No. 9. 1639-1641  [PDF]
  • B. Merialdo, Phonetic recognition using hidden Markov models and maximum mutual information training, in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 1988, pp. 111–114. [PDF]
  • Mamitsuka H. Supervised learning of hidden Markov models for sequence discrimination. Proceedings of the first annual international conference on Computational molecular biology.1997, Santa Fe, New Mexico, United State. Pages: 202 - 208 [PDF]  
  • R. Schlüter, W. Macherey, S. Kanthak, H. Ney, L. Welling, Comparison Of Optimization Methods For Discriminative Training Criteria. In In Proc. EUROSPEECH’97, Vol. 1 (1997), pp. 15-18. [PDF]
  • Pierre Baldi ,  Yves Chauvin. Smooth On-Line Learning Algorithms for Hidden Markov Models (1994) Neural Computation, 6 (2). pp. 307-318  [PDF]
  • Anders Krogh. An Introduction to Hidden Markov Models for Biological Sequences. In Computational Methods in Molecular Biology, edited by S. L. Salzberg, D. B.Searls and S. Kasif, pages 45-63. Elsevier, 1998 [PDF]
  • A. Krogh and S. K. Riis.  Hidden neural networks. Neural Computation, 11(2):541-563, 1999.[PDF]
  • A. Krogh. Two methods for improving performance of a HMM and their application for gene finding. In T. Gaasterland, P. Karp, K. Karplus, C. Ouzounis, C. Sander, and A. Valencia, editors, Proceedings of the Fifth International Conference on Intelligent Systems for Molecular Biology, pages 179-186, Menlo Park, CA, 1997. AAAI Press. [PDF]
  • A. Krogh. Hidden Markov models for labeled sequences. In Proceedings of the 12th IAPR International Conference on Pattern Recognition, pages 140-144, Los Alamitos, California, October 1994. IEEE Computer Society Press.[PDF]
  • S. R. Eddy. Profile Hidden Markov Models. Bioinformatics, 14:755-763, 1998.[PDF]
  • S. R. Eddy. Multiple Alignment Using Hidden Markov Models. In: Proc. Third Int. Conf. Intelligent Systems for Molecular Biology, 114-120. AAAI Press, 1995.[PDF]
  • L. Käll, A. Krogh, and E. L. Sonnhammer. An HMM posterior decoder for sequence feature prediction that includes homology information. Bioinformatics, 21(Suppl. 1):i251-i257, 2005. [PDF]
  • Piero Fariselli, Pier Luigi Martelli and Rita Casadio. A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins.BMC Bioinformatics 2005, 6(Suppl 4):S12 [PDF]
  • Bagos PG, Liakopoulos TD, Hamodrakas SJ. Algorithms for incorporating prior topological information in HMMs: Application to transmembrane proteins. 2006, BMC Bioinformatics; 7:189 [PDF]
  • Bagos PG, Liakopoulos TD, Hamodrakas SJ. Faster Gradient Descent Training of Hidden Markov Models, Using Individual Learning Rate Adaptation. Proceedings of ICGI 2004 Lecture Notes In Artificial Intelligence, Vol. 3264, pp. 40-52 [PDF]
  • Bahl, L.  Brown, P.  de Souza, P.  Mercer, R. Maximum mutual information estimation of hidden Markov model parameters for speech recognition Proceedings of IEEE International Conference on ICASSP '86 Acoustics, Speech, and Signal Processing, 1986 (11): 49 - 52 [PDF]