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Locally Weighted Learning
Authors:Christopher G Atkeson  Andrew W Moore  Stefan Schaal
Affiliation:(1) College of Computing, Georgia Institute of Technology, 801 Atlantic Drive, Atlanta, GA, 30332-0280. E-mail;(2) ATR Human Information Processing Research Laboratories, 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-02, Japan;(3) Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213. E-mail
Abstract: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
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