Radial basis function networks: Generalization in over-realizable and unrealizable scenarios

DOI10.1016/0893-6080(95)00122-0zbMath0869.68079DBLPjournals/nn/FreemanS96OpenAlexW2061435903WikidataQ52198277 ScholiaQ52198277MaRDI QIDQ676295

Jason A. S. Freeman, David Saad

Publication date: 16 March 1997

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0893-6080(95)00122-0

zbMATH Keywords

Bayesian approachtwo-layer radial basis function network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

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• Approximation and estimation bounds for artificial neural networks
• Universal approximation bounds for superpositions of a sigmoidal function
• Learning and generalization in a linear perceptron stochastically trained with noisy data
• Learning a rule in a multilayer neural network
• On Langevin Updating in Multilayer Perceptrons
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities

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