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Classes of feedforward neural networks and their circuit complexity
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) |
| Research Group: | |
| 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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