Learning discontinuities with products-of-sigmoids for switching between local models

M. Toussaint, S. Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Sensorimotor data from many interesting physical interactions comprises discontinuities. While existing locally weighted learning approaches aim at learning smooth functions, we propose a model that learns how to switch discontinuously between local models. The local responsibilities, usually represented by Gaussian kernels, are learned by a product of local sigmoidal classifiers that can represent complex shaped and sharply bounded regions. Local models are incrementally added. A locality prior constrains them to learn only local data---which is the key ingredient for incremental learning with local models.
Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Machine Learning
Number of pages8
ISBN (Print)1-59593-180-5
Publication statusPublished - 2005


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