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Abstract: this article, will be either the boolean alphabet f0; 1g, or the real alphabet R. We denote the set of n-tuples of elements of by (Update)
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.... hardness results hold for neural networks was first shown by Judd [66] In this section we shall prove a simple hardness result from [10, 11] along the lines of one due to Blum and Rivest [36] Neural Computing Surveys 1, 1 47, 1997, http: www.icsi.berkeley.edu jagota NCS...
...to (multilayer) feedforward networks with binary single output. A number of extensions and variations on the basic PAC model have been made [2]. There are extensions for analyzing feedforward linear threshold networks having more than one output node [9] or artificial neural...
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Probabilistic Analysis of Learning in Artificial Neural Networks: .. - Anthony (1994) (Correct)
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BibTeX entry: (Update)
M. Anthony and N. Biggs. Computational learning theory for artificial neural networks. In J. Taylor, editor, Mathematical Approaches to Neural Networks, North Holland Mathematical Library (51), pages 25--62. Elsevier Science Publishers B. V., Amsterdam, 1993. http://citeseer.ist.psu.edu/article/anthony93computational.html More
@misc{ anthony93computational,
author = "M. Anthony and N. Biggs",
title = "Computational learning theory for artificial neural networks",
text = "M. Anthony and N. Biggs. Computational learning theory for artificial neural
networks. In J. Taylor, editor, Mathematical Approaches to Neural Networks,
North Holland Mathematical Library (51), pages 25--62. Elsevier Science
Publishers B. V., Amsterdam, 1993.",
year = "1993",
url = "citeseer.ist.psu.edu/article/anthony93computational.html" }
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