Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Locally Weighted Regression for Control

Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_488
  • 447 Downloads

Synonyms

Definition

This article addresses two topics: learning control and locally weighted regression.

Learning control refers to the process of acquiring a control strategy for a particular control system and a particular task by trial and error. It is usually distinguished from adaptive control (Aström & Wittenmark, 1989) in that the learning system is permitted to fail during the process of learning, resembling how humans and animals acquire new movement strategies. In contrast, adaptive control emphasizes single trial convergence without failure, fulfilling stringent performance constraints, e.g., as needed in life-critical systems like airplanes and industrial robots.

Locally weighted regression refers to supervised learning of continuous functions (otherwise known as function approximation or regression) by means of spatially localized...
This is a preview of subscription content, log in to check access.

Recommended Reading

  1. Aström, K. J., & Wittenmark, B. (1989). Adaptive control. Reading, MA: Addison-Wesley.zbMATHGoogle Scholar
  2. Atkeson, C., Moore, A., & Schaal, S. (1997). Locally weighted learning. AI Review, 11, 11–73.Google Scholar
  3. Atkeson, C. (1989). Using local models to control movement. In Proceedings of the advances in neural information processing systems 1 (pp. 157–183). San Francisco, CA: Morgan Kaufmann.Google Scholar
  4. Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74, 829–836.zbMATHMathSciNetGoogle Scholar
  5. Hastie, T., & Loader, C. (1993). Local regression: Automatic kernel carpentry. Statistical Science, 8, 120–143.Google Scholar
  6. Jordan, M. I., & Jacobs, R. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181–214.Google Scholar
  7. Klanke, S., Vijayakumar, S., & Schaal, S. (2008). A library for locally weighted projection regression. Journal of Machine Learning Research, 9, 623–626.MathSciNetGoogle Scholar
  8. Mitrovic, D., Klanke, S., & Vijayakumar, S. (2008). Adaptive optimal control for redundantly actuated arms. In Proceedings of the 10th international conference on the simulation of adaptive behavior, Osaka, Japan (pp. 93–102). Berlin: Springer-Verlag.Google Scholar
  9. Schaal, S., & Atkeson, C. G. (1998). Constructive incremental learning from only local information. Neural Computation, 10(8), 2047–2084.Google Scholar
  10. Schaal, S., Atkeson, C. G., & Vijayakumar, S. (2002). Scalable techniques from nonparametric statistics. Applied Intelligence, 17, 49–60.zbMATHGoogle Scholar
  11. Ting, J., D’Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., et al. (2005). Predicting EMG data from M1 neurons with variational Bayesian least squares. In Proceedings of advances in neural information processing systems 18, Cambridge: MIT Press.Google Scholar
  12. Ting, J., Kalakrishnan, M., Vijayakumar, S., & Schaal, S. (2008). Bayesian kernel shaping for learning control. In Proceedings of advances in neural information processing systems 21 (pp. 1673–1680). Cambridge: MIT Press.Google Scholar
  13. Ting, J. (2009). Bayesian methods for autonomous learning systems. Ph.D. Thesis, Department of Computer Science, University of Southern California, 2009.Google Scholar
  14. Todorov, E., & Li, W. (2004). A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems. In Proceedings of 1st international conference of informatics in control, automation and robotics, Setúbal, Portugal.Google Scholar
  15. Vijayakumar, S., D’Souza, A., & Schaal, S. (2005). Incremental online learning in high dimensions. Neural Computation, 17, 2602–2634.MathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

There are no affiliations available