Memory-based Stochastic Optimization

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

Volumn , Issue , 1995, Pages 1066-1072

  Moore, Andrew W ^a   Schneider, Jeff ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

REGRESSION ANALYSIS;

BAYESIAN; GLOBAL MODELS; LINEAR REGRESSION ANALYSIS; LOCAL MODEL; LOCAL MODES; NON-LINEAR MODELLING; POLYNOMIAL MODELS; RESPONSE-SURFACE METHODOLOGY; STOCHASTIC OPTIMIZATIONS; WEIGHTED POLYNOMIALS;

OPTIMIZATION;

EID: 85016672777     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. C. G. Atkeson. Using Local Models to Control Movement. In Proceedings of Neural Information Processing Systems Conference, November 1989.
2. S. M. Botros. Model-Based Techniques in Motor Learning and Task Optimization. PhD. Thesis, MIT Dept. of Brain and Cognitive Sciences, February 1994.
3. G. E. P. Box and N. R. Draper. Empirical Model-Building and Response Surfaces, Wiley, 1987.
4. W. S. Cleveland and S. J. Delvin. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting. Journal of the American Statistical Association, 83 (403):596-610, September 1988.
5. M. H, DeGroot. Optimal Statistical Decisions. McGraw-Hill, 1970.
6. J, Gratch, S. Chien, and G. DeJong. Learning Search Control Knowledge for Deep Space Network Scheduling. In Proceedings of the 10th International Conference on Machine Learning. Morgan Kaufmann, June 1993.
7. R. Greiner and I. Jurisica. A statistical approach to solving the BBL utility problem. In Proceedings of the Tenth International Conference on Artificial Intelligence (AAAI-9S). MIT Press, 1992.
8. L. P. Kaelbling. Learning in Embedded Systems. PhD. Thesis; Technical Report No. TR-90-04, Stanford University, Department of Computer Science, June 1990.
9. h. Kushner and D. Clark. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, 1978.
10. O. Maron and A. Moore. Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation, In Advances in Neural Information Processing Systems 6. Morgan Kaufmann, December 1993.
11. A. W, Moore, C. G. Atkeson, and S. Schaal. Memory-based Learning for Control. Technical report, CMU Robotics Institute, Technical Report CMU-RI-TR-S5-18 (Submitted for Publication), 1995.
12. A. W. Moore. Fast, Robust Adaptive Control by Learning only Forward Models. In J. E. Moody, S. J. Hanson, and R. P. Lippman, editors, Advances in Neural Information Processing Systems 4. Morgan Kaufmann, April 1992.
13. h. Robbins and S. Monro. A stochastic approximation method. Annals of Mathematical Statistics, 22:400-407, 1951.
14. M. Salganicoff and L. h. Ungar. Active Exploration and Learning in Real-Valued Spaces using Multi-Armed Bandit Allocation Indices, In Proceedings of the 12th Internotional Conference on Machine Learning. Morgan Kaufmann, 1995.