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Locally weighted learning




This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control. Keywords: locally weighted regression, LOESS, LWR, lazy learning, memory-based learning, least commitment learning, distance functions, smoothing parameters, weighting functions, global tuning, local tuning, interference.

Author(s): Atkeson, C. G. and Moore, A. W. and Schaal, S.
Book Title: Artificial Intelligence Review
Volume: 11
Number (issue): 1-5
Pages: 11-73
Year: 1997

Department(s): Autonomous Motion
Bibtex Type: Article (article)

Cross Ref: p41
Note: clmc
URL: http://www-clmc.usc.edu/publications/A/atkeson-AIR-I-1997.pdf


  title = {Locally weighted learning},
  author = {Atkeson, C. G. and Moore, A. W. and Schaal, S.},
  booktitle = {Artificial Intelligence Review},
  volume = {11},
  number = {1-5},
  pages = {11-73},
  year = {1997},
  note = {clmc},
  crossref = {p41},
  url = {http://www-clmc.usc.edu/publications/A/atkeson-AIR-I-1997.pdf}