DocumentCode
3158924
Title
Nonparametric classification using radial basis function nets and empirical risk minimization
Author
Krzyzak, A. ; Linder, T. ; Lugosi, G.
Author_Institution
Dept. of Comput. Sci., Concordia Univ., Montreal, Que., Canada
Volume
2
fYear
1994
fDate
9-13 Oct 1994
Firstpage
72
Abstract
In the paper convergence properties of radial basis function (RBF) networks are studied for a large class of basis functions. The universal approximation property of the nets is shown. Parameters of RBF nets are learned through empirical risk minimization. The optimal nets are shown to be consistent in nonparametric classification. The tools used in the analysis include Vapnik-Chervonenkis (VC) dimension and the covering numbers
Keywords
feedforward neural nets; Vapnik-Chervonenkis dimension; convergence; covering numbers; empirical risk minimization; nonparametric classification; radial basis function nets; risk minimization; universal approximation; Computer science; Convergence; Estimation error; Kernel; Mathematics; Neural networks; Radial basis function networks; Random variables; Risk management; Virtual colonoscopy;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on
Conference_Location
Jerusalem
Print_ISBN
0-8186-6270-0
Type
conf
DOI
10.1109/ICPR.1994.576878
Filename
576878
Link To Document