DocumentCode
2259505
Title
Comparison of rates of linear and neural network approximation
Author
Kurkova, Vera ; Sanguineti, Marcello
Author_Institution
Inst. of Comput. Sci., Czechoslovak Acad. of Sci., Prague, Czech Republic
Volume
1
fYear
2000
fDate
2000
Firstpage
277
Abstract
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
Keywords
Hilbert spaces; feedforward neural nets; function approximation; perceptrons; linear approximation; lower bounds; multivariable functions; n-dimensional linear subspaces; neural network approximation; perceptron networks; variable basis function approximation; worst-case errors; Computer errors; Computer networks; Computer science; Electronic mail; Feedforward neural networks; Fourier transforms; Function approximation; Linear approximation; Neural networks; Upper bound;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.857848
Filename
857848
Link To Document