Locally Weighted Learning Algorithm
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A Locally Weighted Learning Algorithm is a lazy learning algorithm that has been applied to control task.
- Example(s):
- Counter-Example(s):
See: Learning Control, Adaptive Control, Local Distance Metric Adaptation Algorithm, Training Algorithm, Weight Function.
References
2017
- (Ting et al., 2017) ⇒ Jo-Anne Ting, Franzisk Meier, Sethu Vijayakumar, and Stefan Schaal (2017) "Locally Weighted Regression for Control". In: Sammut & Webb. (2017).
- QUOTE: Locally weighted regression refers to supervised learning of continuous functions (otherwise known as function approximation or regression) by means of of spatially localized algorithms, which are often discussed in the context of kernel regression, nearest neighbor methods, or lazy learning (Atkeson et al. 1997). Most regression algorithms are global learning systems(...).
In contrast, local learning systems conceptually split up the global learning problem into multiple simpler learning problems. Traditional locally weighted regression approaches achieve this by dividing up the cost function into multiple independent local cost functions (...)
- QUOTE: Locally weighted regression refers to supervised learning of continuous functions (otherwise known as function approximation or regression) by means of of spatially localized algorithms, which are often discussed in the context of kernel regression, nearest neighbor methods, or lazy learning (Atkeson et al. 1997). Most regression algorithms are global learning systems(...).
1997
- (Atkeson et al., 1997) ⇒ Christopher G. Atkeson, Andrew W. Moore, and Stefan Schaal. (1997). "Locally Weighted Learning for Control". In: Aha D.W. (eds) Lazy Learning. Springer, Dordrecht.
- ABSTRACT: Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.