An efficient learning algorithm for function approximation with radial basis function networks

Autor:	Shien-Ching Hwang, Yen-Jen Oyang
Rok vydání:	2004
Předmět:	Radial basis function network Function approximation Computational learning theory business.industry Radial basis function kernel Activation function Basis function Artificial intelligence business Algorithm Time complexity Hierarchical RBF Mathematics
Zdroj:	Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..
DOI:	10.1109/iconip.2002.1198218
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0b099f4e083f575f204433c5fe83aa15 https://doi.org/10.1109/iconip.2002.1198218 Zobrazit plný text záznamu