Representations and rates of approximation of real-valued Boolean functions by neural networks

Representations and rates of approximation of real-valued Boolean functions by neural networks

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Abstract

We give upper bounds on rates of approximation of real-valued functions of d Boolean variables by one-hidden-layer perceptron networks. Our bounds are of the form c/n where c depends on certain norms of the function being approximated and n is the number of hidden units. We describe sets of functions where these norms grow either polynomially or exponentially with d.

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Authors
  • Kurkova, Vera
  • Savicky, Petr
  • Hlavackova-Schindler, Katerina
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Shortfacts
Category
 Journal Paper
Divisions
 Data Mining and Machine Learning
Subjects
 Kuenstliche Intelligenz
Journal or Publication Title
 Neural Networks
ISSN
 0893-6080
Publisher
 Elsevier Science Ltd.
Page Range
 pp. 651-659
Number
 4
Volume
 11
Date
 1998
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