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.
Index Terms:
linear and neural network approximation, Kolmogorov width, dimension-independent rates of approximation, perceptron networks
Citation:
Vera Kurková, Marcello Sanguineti, "Comparison of Rates of Linear and Neural Network Approximation," ijcnn, vol. 1, pp.1277, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 1, 2000