Fast state discovery for HMM model selection and learning

Journal of Machine Learning Research

Volumn 2, Issue , 2007, Pages 492-499

  Siddiqi, Sajid M ^a   Gordon, Geoffrey J ^b   Moore, Andrew W ^c  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b Google Inc ^*  (United States)

^c GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM MODEL; DYNAMIC CONTEXTS; FAST STATE; HIDDEN STATE; HMM MODELS; ITS EFFICIENCIES; LOCAL MINIMUMS; MODEL PARAMETERS; MODEL SEARCH; MODEL SELECTION; NUMBER OF STATE; REAL WORLD DATA; SYNTHETIC DATA; TEST SETS; TOP-DOWN METHODS;

ARTIFICIAL INTELLIGENCE; HIDDEN MARKOV MODELS; TOPOLOGY; VIRTUAL REALITY;

ALGORITHMS;

EID: 84862287133     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (18)

1. D.J. Newman, S. Hettich, C. B., & Merz, C. (1998). UCIH repository of machine learning databases.
2. Elidan, G., & Friedman, N. (2001). Learning the dimensionality of hidden variables. Proc. UAI.
3. Felzenszwalb, P., Huttenlocher, D., & Kleinberg, J. (2003). Fast Algorithms for Large State Space HMMs with Applications to Web Usage Analysis. Advances in Neural Information Processing Systems (NIPS).
4. Howard, A., & Roy, N. (2003). The robotics data set repository (radish).
5. Kadous, M. W. (2002). Temporal classification: Extending the classification paradigm to multivariate time series. Doctoral dissertation, University of New South Wales.
6. Krogh, A., Mian, I., & Haussler, D. (1994). A hidden Markov model that finds genes in E. coli DNA. Nucleic Acids Research, 22, 4768-4778.
7. Li, C., & Biswas, G. (1999). Temporal pattern generation using hidden markov model based unsupervised classification (pp. 245-256. ).
8. Ney, H. (1990). Acoustic modeling of phoneme units for continuous speech recognition. Proc. 5th Europ. Signal Processing Conference.
9. Ostendorf, M., & Singer, H. (1997). Hmm topology design using maximum likelihood successive state splitting. Computer Speech and Language, 11, 17-41.
10. Rabiner, L. R. (1989). A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. IEEE, 77, 257-285.
11. Ren, L., Patrick, A., Efros, A. A., Hodgins, J. K., & Rehg, J. M. (2005). A data-driven approach to quantifying natural human motion. SIGGRAPH 2005, 24, 1090-1097.
12. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics.
13. Seymore, K., McCallum, A., & Rosenfeld, R. (1999). Learning hidden Markov model structure for information extraction. AAAI'99 Wkshp Machine Learning for Information Extraction.
14. Sharma, A., Morales, D., Kantor, G., & Choset, H. (2005). Towards removing artificial landmarks for autonomous exploration in structured environments. Master's thesis, Carnegie Mellon University.
15. Siddiqi, S. M., & Moore, A. W. (2005). Fast inference and learning in large-state-space HMMs. Proc. ICML.
16. Starner, T., & Pentland, A. (1995). Visual Recognition of American Sign Language Using Hidden Markov Models. Proc., Intl. Workshop on Automatic Face and Gesture Recognition (IWAFGR).
17. Stolcke, A., & Omohundro, S. (1994). Best-first Model Merging for Hidden Markov Model Induction Technical Report TR-94-003). Intl. Computer Science Institute.
18. Viterbi, A. J. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, IT13, 260-267.