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28 March 2024
 
  » arxiv » nlin.AO/0202039

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A neural model for multi-expert architectures
Marc Toussaint ;
Date 19 Feb 2002
Journal Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), 2755-2760.
Subject Adaptation and Self-Organizing Systems; Neural and Evolutionary Computing; Disordered Systems and Neural Networks | nlin.AO cond-mat.dis-nn cs.NE
AbstractWe present a generalization of conventional artificial neural networks that allows for a functional equivalence to multi-expert systems. The new model provides an architectural freedom going beyond existing multi-expert models and an integrative formalism to compare and combine various techniques of learning. (We consider gradient, EM, reinforcement, and unsupervised learning.) Its uniform representation aims at a simple genetic encoding and evolutionary structure optimization of multi-expert systems. This paper contains a detailed description of the model and learning rules, empirically validates its functionality, and discusses future perspectives.
Source arXiv, nlin.AO/0202039
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