From Isolation to Cooperation: An Alternative View of a System of Experts

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

Volumn , Issue , 1995, Pages 605-611

  Schaal, Stefan ^a,b   Atkeson, Christopher C ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BLENDING; FEATURE EXTRACTION; LEARNING SYSTEMS;

CROSS VALIDATION ERRORS; DISTANCE METRICS; INCREMENTAL LEARNING; INDIVIDUAL PREDICTION; INPUT FEATURES; MODELING DATA; RECEPTIVE FIELDS; REGRESSION PROBLEM; SECOND-ORDER METHODS; SIZE AND SHAPE;

FORECASTING;

EID: 84964009081     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Atkeson, C. G., Moore, A. W., & Schaal, S. (submitted). "Locally weighted learning." Artificial Intelligence Review.
2. Atkeson, C. G, (1992). "Memory-based approaches to approximating continuous functions." In; Casdagli, M., & Eubank, S. (Eds.), Nonlinear Modeling and Forecasting, pp. 503-521. Addison Wesley.
3. Belsley, D. A., Kuh, E., & Welsch. R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinear'tty. New York: Wiley.
4. Cleveland, W. S. (1979). "Robust locally weighted regression and smoothing scatterplots." J. American Stat. Association, 74, pp. 829-836.
5. de Boor, C. (1978). A practical guide to splines. New York: Springer.
6. Hastie, T. J., & Tibshirani, R. J. (1990). Generalized additive models. London: Chapman and Hall.
7. Jacobs, R, A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). "Adaptive mixtures of local experts." Neural Computation, 3, pp. 79-87.
8. Jordan, M. I., & Jacobs, R. (1994). "Hierarchical mixtures of experts and the EM algorithm." Neural Computation. 6, pp. 79-87.
9. Ljung, L., & S_derstr_m, T. (1986). Theory and practice of recursive identification. Cambridge, MIT Press.
10. McLachlan, G. J., & Basford, K. E, (1988). Mixture models. New York; Marcel Dekker.
11. Nadaraya, E. A. (1964), "On estimating regression. " Theor. Prob. Appl., 9, pp. 141-142.
12. Schaal. S.. & Atkeson, C, G. (1994b). "Assessing the quality of learned local models." In: Cowan, J., Tesauro, G., & Alspector, J. (Eds,), Advances in Neural Information Processing Systems 6. Morgan Kaufmann.
13. Scott, D. W. (1992). Multivariate Density Estimation. New York: Wiley.
14. Sutton, R. S. (1992). "Gain adaptation beats least squares." In: Proc. of 7th Yale Workshop on Adaptive and Learning Systems, New Haven, CT.
15. Wolpert, D. H. (1990). "Stacked genealization. " Los Alamos Technical Report LA-UR-90-3460.