Learning in Deep Radial Basis Function Networks

Learning in neural networks with locally-tuned neuron models such as radial Basis Function (RBF) networks is often seen as instable, in particular when multi-layered architectures are used. Furthermore, universal approximation theorems for single-layered RBF networks are very well established; there...

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Bibliographic Details
Published in:Entropy
Main Authors: Fabian Wurzberger, Friedhelm Schwenker
Format: Article
Language:English
Published: MDPI AG 2024-04-01
Subjects:
radial basis function networks
function approximation
function interpolation
classification
Mahalanobis distance
partially connected neural networks
Online Access:https://www.mdpi.com/1099-4300/26/5/368

Internet

https://www.mdpi.com/1099-4300/26/5/368

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