DocumentCode
288493
Title
Some results on L1 convergence rate of RBF networks and kernel regression estimators
Author
Krzyzak, Adam ; Xu, Lei
Author_Institution
Dept. of Comput. Sci., Concordia Univ., Montreal, Que., Canada
Volume
2
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
1209
Abstract
Rather than studying the L2 convergence rates of kernel regression estimators (KRE) and radial basis function (RBF) nets given in Xu-Krzyzak-Yuille (1992 & 1993), we study convergence properties of the mean integrated absolute error (MIAE) for KRE and RBF nets. It has been shown that MIAE of KRE and RBF nets can converge to zero as the size of networks and the size of the training sequence tend to ∞, and that the upper bound for the convergence rate of MIAE is O(n-αs/(2+s)( 2α+d)) for approximating Lipschitz functions
Keywords
convergence of numerical methods; estimation theory; feedforward neural nets; learning (artificial intelligence); statistical analysis; L1 convergence rate; approximating Lipschitz functions; kernel regression estimators; mean integrated absolute error; radial basis function nets; training sequence; upper bound; Convergence; Estimation error; Kernel; Neural networks; Probability; Radial basis function networks; Symmetric matrices; Upper bound;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374356
Filename
374356
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