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NECO
1998

Constructive Incremental Learning from Only Local Information

9 years 10 months ago
Constructive Incremental Learning from Only Local Information
We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, the size and shape of the receptive field of each locally linear model as well as the parameters of the locally linear model itself are learned independently, i.e., without the need for competition or any other kind of communication. Independent learning is accomplished by incrementally minimizing a weighted local cross validation error. As a result, we obtain a learning system that can allocate resources as needed while dealing with the bias-variance dilemma in a principled way. The spatial localization of the linear models increases robustness towards negative interference. Our learning system can be interpreted as a nonparametric adaptive bandwidth smoother, as a mixture of experts where the experts are trained in isolation, and as a learning system which profits from combining independent expert knowledge ...
Stefan Schaal, Christopher G. Atkeson
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 1998
Where NECO
Authors Stefan Schaal, Christopher G. Atkeson
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