Title :
An efficient learning algorithm for function approximation with radial basis function networks
Author :
Oyang, Yen-Jen ; Hwang, Shien-Ching
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan
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.
Keywords :
computational complexity; function approximation; learning (artificial intelligence); radial basis function networks; RBF network; function approximation; input vectors; learning algorithm; radial basis function networks; space complexity; time complexity; vector space; Application software; Approximation algorithms; Computer science; Data mining; Equations; Function approximation; Linear approximation; Machine learning; Machine learning algorithms; Radial basis function networks;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1198218