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Classes of feedforward neural networks and their circuit complexity

✍ Scribed by John S. Shawe-Taylor; Martin H.G. Anthony; Walter Kern

Publisher: Elsevier Science
Year: 1992
Tongue: English
Weight: 594 KB
Volume: 5
Category: Article
ISSN: 0893-6080
DOI: 10.1016/s0893-6080(05)80093-0

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✦ Synopsis

Th& paper aims to p&ce neural networks in the conte.\t ol'booh'an citz'ldt complexit.l: 1,1~, de/itte aplm~priate classes qlfeedybrward neural networks with specified fan-in, accm'ac)' olcomputation and depth and ttsing techniques" o./commzmication comph:¥ity proceed to show t/tat the classes.fit into a well-studied hieralz'h)' q/boolean circuits. Results cover both classes of sigmoid activation./hnction networks and linear threshold networks. Tiffs provides a much needed theoretical basis./or the study o/the computational power qlilbed[brward neural networks.

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