Generalized associative mixture of experts
نویسنده
چکیده
Modular learning, inspired by divide and conquer, learns a large number of localized simple concepts (classiiers or function approximators) as against single complex global concept. As a result, modular learning systems are eecient in learning and eeective in generalization. In this work, a general model for modular learning systems is proposed whereby, specialization and localization is induced in the modules by associating them with training patterns. A case study of this model leading to a Generalized Associative Mixture of Experts (GAMES) is undertaken. For the supervised learning task of function approximation, GAMES has shown signiicant improvements over single layer and hierarchical mixture of experts, both in training eeciency and generalization performance, for various artiicial training set.
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