Title :
OLS versus SVM approach to learning of RBF networks
Author :
Osowski, Stanislaw ; Markiewicz, Tomasz
Author_Institution :
Warsaw Univ. of Technol., Poland
fDate :
31 July-4 Aug. 2005
Abstract :
The paper presents the comparative analysis of the learning algorithms of the radial basis function (RBF) neural networks. Two best adaptive algorithms are considered. One is based on the orthogonal least square (OLS) applying Gram-Schmidt orthogonalization and the second is relying on the support vector machine (SVM) approach. The results of numerical experiments in the classification and regression modes are presented and discussed in the paper.
Keywords :
learning (artificial intelligence); least mean squares methods; radial basis function networks; support vector machines; Gram-Schmidt orthogonalization; RBF networks; SVM approach; learning algorithms; neural networks; orthogonal least square; radial basis function; support vector machine approach; Algorithm design and analysis; Least squares approximation; Least squares methods; Matrix decomposition; Multidimensional systems; Neurons; Paper technology; Radial basis function networks; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1555998
