An efficient learning algorithm for function approximation with radial basis function networks
Journal
ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
Journal Volume
2
ISBN
9810475241
Date Issued
2002-01-01
Author(s)
Hwang, Shien Ching
Abstract
This paper proposes a novel learning algorithm for constructing function approximators with radial basis function (RBF) networks. In comparison with the existing learning algorithms, the proposed algorithm features lower time complexity for constructing the RBF network and is able to deliver the same level of accuracy. The time taken by the proposed algorithm to construct the RBF network is in the order of O(|S|), where S is the set of training samples. As far as the time complexity for predicting the function values of input vectors is concerned, the RBF network constructed with the proposed learning algorithm can complete the task in O(|T|), where T is the set of input vectors. Another important feature of the proposed learning algorithm is that the space complexity of the RBF network constructed is O(m|S|), where m is the dimension of the vector space in which the target function is defined.
Subjects
Function approximation | Machine learning | Radial basis function network
Type
conference paper