Self-similar layered hidden markov models

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

Volumn 2168, Issue , 2001, Pages 1-15

  Adibi, Jafar ^a   Shen, Wei Min ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; DATA MINING;

COMPLEX PROBLEMS; LAYERED HIDDEN MARKOV MODELS; MODEL CONSTRUCTION; REAL-WORLD NETWORKS; SELF SIMILAR PROPERTIES; SELF-SIMILAR; SELF-SIMILARITIES; SYNTHETIC DATA;

HIDDEN MARKOV MODELS;

EID: 27944498948     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44794-6_1     Document Type: Conference Paper

References (24)

1. Adibi, J., Shen, W-M. General structure of mining through layered phases. submitted to International Conference on Data Engineering. (2002). San Jose, California, USA: IEEE.
2. Barbara, D. Chaotic Mining: Knowledge discovery using the fractal dimension. in ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD). (1999). Philadelphia, USA,.
3. Bertsekas, D. C., Tsitsiklis, N. J., Parallel and distributed computation: Numerical Methods. (1989), Englewood Clifffs, New Jersey: Printice-Hall.
4. Boutilier, R. I., Brafman, I., Geib, C. Prioritized goal decomposition of Markov Decision Processes: Toward a synthesis of classical and decision theoretic planning. in IJCAI-97. (1997). Nagoya, Japan.
5. Brand, N., Oliver, N., and Pentland, A.,. Coupled Hidden Markov Models for complex action recognition. in CVPR. (1997).
6. Chang, J. W., Glass, J, Segmentation and modeling in segment-based recognition. (1997).
7. Cohen, J., Segmentation speech using dynamic programming. ASA, (1981). 69(5): p. 1430-1438.
8. Dean, T. L., S. H. Decomposition techniques for planning in stochastic domains. in IJCAI. (1995). Montreal, CA.
9. Faloutsos, C., Seeger, B., Traina, A. and Traina Jr., C. Spatial Join selectivity using power law. in SIGMOD. (2000). Dallas, TX.
10. Feldmann, A., Gilbert, A. C., Willinger, W., Kurtz, T. G., The changing nature of Network traffic: Scaling phenomena. ACM Computer Communication Review, (1998). 28(April): p. 5-29.
11. Fine, S., Singer Y, Tishby N, The hierarchical Hidden Markov Model: Analysis and applications. Machine Learning, (1998). 32(1): p. 41-62.
12. Ghahramani, Z., Jordan, M., Factorial Hidden Markov Models. Machine Learning, (1997). 2: p. 1-31.
13. Holmes, W. J., Russell, M. L., Probablistic-trajectory segmental HMMs. Computer Speech and Languages, (1999). 13: p. 3-37.
14. Leland, W., Taqqu, M., Willinger, W., Wilson, D. On the self-similar nature of Ethernet traffic. in ACM SIGComm. (1993). San Francisco, CA.
15. Oates, T. Identifying distinctive subsequences in multivariate time series by clustering. In KDD-99. (1999). San Diego, CA: ACM.
16. Pearl, J., Probabilistic reasoning in intelligence: Network of plausible inference. (1988), San Mateo, CA: Morgan Kaufmann.
17. Precup, D., Sutton, R. S., Multi-time models for temporally abstract planning, in NIPS-11, M. Moser, Jordan, M. Petsche, T., Editor. 1998, MIT Press: Cambridge.
18. Puterman, M. L., Markov Decision Process: discrete stochastic dynamic programming. (1994), New Yourk: Wiley.
19. Rabiner, L. R., A tutorial on Hidden Markov Models and selected applications in speech recognition. IEEE, (1989). 7(2): p. 257-286.
20. Sutton, R., Barto, A., Reinforcement learning: An Introduction. (1998), Cambridge: MIT Press.
21. Traina, C., Traina, A., Wu, L., and Faloutsos, C. Fast feature selection using the fractal dimension. in XV Brazilian Symposium on Databases (SBBD). (2000). Paraiba, Brazil.
22. Vogler, C., Metaxas, D. Parallel Hidden Markov Models for American Sign Language recognition. in International Conference on Computer Vision. (1999). Kerkyra, Greece.
23. Williams, C., Hinton, G. E., ed. Mean field networks that leat discriminate temporally distorted strings., ed. D. Touretzkey, Elman, J., Sejnowski, T. and Hinton G. (1991).
24. Willinger, W., Taqqu M. S., Erramilli, A., ed. A bibliographical guide to self-similar trace and performance modeling for modern high-speed networks. Stochastic Networks: Theory and Applications, ed. F. P. Kelly, Zachary, S. and Ziedins, I. (1996), Clarendon Press, Oxford University Press: Oxford. 339-366.