Adaptive Mixtures of Local Experts
Abstract
We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently different approaches. We demonstrate that the learning procedure divides up a vowel discrimination task into appropriate subtasks, each of which can be solved by a very simple expert network.
Cite
Text
Jacobs et al. "Adaptive Mixtures of Local Experts." Neural Computation, 1991. doi:10.1162/NECO.1991.3.1.79Markdown
[Jacobs et al. "Adaptive Mixtures of Local Experts." Neural Computation, 1991.](https://mlanthology.org/neco/1991/jacobs1991neco-adaptive/) doi:10.1162/NECO.1991.3.1.79BibTeX
@article{jacobs1991neco-adaptive,
title = {{Adaptive Mixtures of Local Experts}},
author = {Jacobs, Robert A. and Jordan, Michael I. and Nowlan, Steven J. and Hinton, Geoffrey E.},
journal = {Neural Computation},
year = {1991},
pages = {79-87},
doi = {10.1162/NECO.1991.3.1.79},
volume = {3},
url = {https://mlanthology.org/neco/1991/jacobs1991neco-adaptive/}
}