Clustering sequences with hidden Markov models

Advances in Neural Information Processing Systems

Volumn , Issue , 1997, Pages 648-654

  Smyth, Padhraic ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER STRUCTURE; DETERMINING THE NUMBER OF CLUSTERS; FEATURE SPACE; HIDDEN MARKOV MODELS (HMMS); INITIALIZATION PROCEDURES; MIXTURE MODEL; MODEL BASED APPROACH;

HIDDEN MARKOV MODELS;

EID: 84898995769     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Baldi, P. and Y. Chauvin, Hierarchical hybrid modeling, HMM/NN architectures, and protein applications, Neural Computation, 8(6), 1541-1565, 1996.
2. Krogh, A. et al., Hidden Markov models in computational biology: applications to protein modeling, it J. Mol. Bio., 235:1501-1531, 1994.
3. Juang, B. H., and L. R. Rabiner, A probabilistic distance measure for hidden Markov models, AT&T Technical Journal, vol.64, no.2, February 1985.
4. Rabiner, L. R., C. H. Lee, B. H. Juang, and J. G. Wilpon, HMM clustering for connected word recognition,Proc. Int. Conf. Ac. Speech. Sig. Proc, IEEE Press, 405-408, 1989.
5. Shao, J., Linear model selection by cross-validation,J. Am. Stat. Assoc., 88(422), 486-494, 1993.
6. Smyth, P., Clustering using Monte-Carlo cross validation, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Menlo Park, CA: AAAI Press, pp. 126-133, 1996.
7. Zeevi, A. J., Meir, R., Adler, R., Time series prediction using mixtures of experts, in this volume, 1997.