DocumentCode
3173787
Title
On kernel and radial basis function techniques for classification and function recovering
Author
Pawlak, M. ; Ng, M. F Yat Fung
Author_Institution
Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada
Volume
2
fYear
1994
fDate
9-13 Oct 1994
Firstpage
454
Abstract
We describe alternative learning schemes for radial basis function (RBF) networks both in the context of classification and function recovering problems. The connection between RBF networks and well established kernel-type regression estimators is exhibited. We show that the asymptotic accuracy (convergence and rate of convergence) of the proposed RBF nets can be equivalent to the kernel technique
Keywords
pattern classification; RBF networks; asymptotic accuracy; classification; convergence; function recovery; kernel-type regression estimators; learning schemes; radial basis function networks; Algorithm design and analysis; Computational efficiency; Convergence; Electronic mail; Feedforward neural networks; Guidelines; Kernel; Multi-layer neural network; Neural networks; Radial basis function networks;
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.1994.576980
Filename
576980
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