Adaptive Mixtures of Local Experts

Robert A. Jacobs; Michael I. Jordan; Steven J. Nowlan; Geoffrey E. Hinton

doi:10.1162/neco.1991.3.1.79

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.

Keywords

Computer scienceSimple (philosophy)Artificial intelligenceTask (project management)Set (abstract data type)Associative propertyMachine learningModular designArtificial neural networkCompetitive learningSupervised learningAssociative learningMathematicsPsychology

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Publication Info

Year: 1991
Type: article
Volume: 3
Issue: 1
Pages: 79-87
Citations: 4563
Access: Closed

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APA Style

                            
                                
                                    Robert A. Jacobs, 
                                
                                    Michael I. Jordan, 
                                
                                    Steven J. Nowlan
                                
                                et al.
                            
                            (1991). 
                            Adaptive Mixtures of Local Experts. 
                            Neural Computation
                            , 3
                            (1)
                            , 79-87.
                            https://doi.org/10.1162/neco.1991.3.1.79
                        

Identifiers

DOI: 10.1162/neco.1991.3.1.79