Classes of feedforward neural networks and their circuit complexity

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Shawe-Taylor, John S. and Anthony, Martin H.G. and Kern, Walter (1992) Classes of feedforward neural networks and their circuit complexity. Neural Networks, 5 (6). pp. 971-977. ISSN 0893-6080

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Abstract:This paper aims to place neural networks in the context of boolean circuit complexity. We define appropriate classes of feedforward neural networks with specified fan-in, accuracy of computation and depth and using techniques of communication complexity proceed to show that the classes fit into a well-studied hierarchy of boolean circuits. Results cover both classes of sigmoid activation function networks and linear threshold networks. This provides a much needed theoretical basis for the study of the computational power of feedforward neural networks.
Item Type:Article
Copyright:© 1992 Elsevier Science
Faculty:
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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Link to this item:http://purl.utwente.nl/publications/57459
Official URL:http://dx.doi.org/10.1016/S0893-6080(05)80093-0
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