Incremental supervised learning: localized updates in nonlocalized networks

Wendy Foslien; Tariq Samad

doi:10.1117/12.140156

1 July 1992 Incremental supervised learning: localized updates in nonlocalized networks

Wendy Foslien, Tariq Samad

Proceedings Volume 1710, Science of Artificial Neural Networks; (1992) https://doi.org/10.1117/12.140156
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

We present a novel yet simple approach to incremental learning in neural networks: the problem of updating a mapping based on limited new data. The approach consists of forming a training set by appending to the new data additional training examples generated by exercising the network. This strategy enables the mapping to be updated in the neighborhood of the new data without causing distortions elsewhere in the input space. The approach can be used with any neural network model; it is particularly useful for the popular multilayer sigmoidal networks in which small parameter changes can have nonlocal consequences. Demonstrations and parametric explorations on a toy problem are described.

Citation Download Citation

Wendy Foslien and Tariq Samad "Incremental supervised learning: localized updates in nonlocalized networks", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140156

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