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Abstract: This report surveys some connections between Boolean functions and artificial neural networks. The focus is on cases in which the individual neurons are linear threshold neurons, sigmoid neurons, polynomial threshold neurons, or spiking neurons. We explore the relationships between types of artificial neural network and classes of Boolean function. In particular, we investigate the type of Boolean functions a given type of network can compute, and how extensive or expressive the set of... (Update)
Active bibliography (related documents): More All
1.1: Analysis of Data with Threshold Decision Lists - Martin Anthony December (Correct)
0.9: On Computing Boolean Functions by a Spiking Neuron - Schmitt (1998) (Correct)
0.6: Complexity of Boolean Computations - For Spiking Neuron (Correct)
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0.1: Function Learning from Interpolation - Anthony, Bartlett (1995) (Correct)
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BibTeX entry: (Update)
@misc{ department-boolean,
author = "Martin Anthony Department",
title = "Boolean Functions and Artificial Neural Networks",
url = "citeseer.ist.psu.edu/616581.html" }
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Documents on the same site (http://www.maths.lse.ac.uk/Personal/martin/mresearch.html): More
A Result of Vapnik with Applications - Anthony, Shawe-Taylor (1991) (Correct)
Computational Learning Theory for Artificial Neural Networks - Anthony, Biggs (1993) (Correct)
Accuracy of Techniques for the Logical Analysis of Data - Anthony (Correct)
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