LOCALLY WEIGHTED LEARNING

This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regr ession. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the es timates and bias, assessing predictions, handling noisy data and outli ers, improving the quality of predictions by tuning fit parameters, in terference between old and new data, implementing locally weighted lea rning efficiently, and applications of locally weighted learning. A co mpanion paper surveys how locally weighted learning can be used in rob ot learning and control.