Learning Fine Motion by Markov Mixtures of Experts

NIPS 1995: Proceedings of the 8th International Conference on Neural Information Processing Systems

Volumn , Issue , 1995, Pages 1003-1009

  Meilǎ, Marina ^a   Jordan, Michael I ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

'CURRENT; DISCRETE HMM; GRADIENT ASCENT; HIDDEN STATE VARIABLES; LEARN+; MANIPULATION TASK; MIXTURE OF EXPERTS; PRIOR-KNOWLEDGE; ROBOT ARMS; TRANSITION PROBABILITIES;

LEARNING ALGORITHMS;

EID: 85156193355     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

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2. Dempster, A. P., Laird, N. M, and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B, 39:1-38.
3. Eberman, B. S. (1995). A sequential decision approach to sensing manipulation contact features. PhD thesis, M. I. T., Dept. of Electrical Engineering.
4. Jacobs. R. A., Jordan, M. I., Nowlan, S., & Hinton, G. E, (1991), Adaptive mixtures of local experts. Neural Computation, 3, 1-12.
5. Lozano-Perez, T. (1983). Spatial planning: a configuration space approach. IEEE Transactions on Computers.
6. Mason, M. T. (1981). Compliance and force control for computer controlled manipulation. IEEE Trans. on Systems, Man and Cybernetics.
7. Meila, M. and Jordan, M. I. (1994). Learning the parameters of HMMs with auxiliary input. Technical Report 9401, MIT Computational Cognitive Science, Cambridge, MA.
8. Rabiner, R. L. and Juang, B. H. (1986), An introduction to hidden Markov models. ASSP Magazine, 3 (1):4-16.