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Comparison of rates of linear and neural network approximation

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2 Author(s)
Kurkova, V. ; Inst. of Comput. Sci., Czechoslovak Acad. of Sci., Prague, Czech Republic ; Sanguineti, M.

We develop some mathematical tools for comparison of rates of fixed versus variable basis function approximation. Using these tools, we describe sets of multivariable functions, for which lower bounds on worst-case errors in approximation by n-dimensional linear subspaces are larger than upper bounds on such errors in approximation by perceptron networks with n hidden units

Published in:

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on  (Volume:1 )

Date of Conference:

2000

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