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
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;
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
DOI :
10.1109/ICPR.1994.576980
