Baum-Welch learning in discrete Hidden Markov Models with linear factorial constraints

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2415 LNCS, Issue , 2002, Pages 1180-1185

  Otterpohl, Jens R ^a  

^a UNIVERSITY OF BREMEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS; TRELLIS CODES; ALGORITHMS; MARKOV PROCESSES;

BAUM-WELCH; BAUM-WELCH ALGORITHMS; CARTESIAN PRODUCTS; DISCRETE HIDDEN MARKOV MODEL; HIDDEN STATE; POTENTIAL BENEFITS; TIME-SCALES; TRANSFORMATION BASED;

HIDDEN MARKOV MODELS;

EID: 33745593176     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-46084-5_191     Document Type: Conference Paper

References (6)

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2. Baum, L. E., Petrie, T ., Soules, G. &Weiss, N. (1970) A Maximization Techniques Ocurring in the Statistical Analysis of Probabilistic Functions of Markov Chains. The Annals of Mathematical Statistics 41 (1):164-171.
3. Ghahramani, Z. & Jordan, M. I. (1997) Factorial Hidden Markov Models. Machine Learning 29: 245-273.
4. Levinson, S. E., Rabiner, L. R.& Sondhi, M. M. (1983) An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition. The Bell Systems Technical Journal 62(4):1035-1074.
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6. Roweis, S. Constrained Hidden Markov Models (2000). Advances in Neural Information Processing Systems 12, Solla, S. A., Leen, T. K., Müller, K.-R., eds., MIT Press.