title: A global learning algorithm for a RBF network.
abstract: This article presents a new learning algorithm for the construction and training of a RBF neural network. The algorithm is based on a global mechanism of parameter learning using a maximum likelihood classification approach. The resulting neurons in the RBF network partitions a multidimensional pattern space into a set of maximum-size hyper-ellipsoid subspaces in terms of the statistical distributions of the training samples. An important feature of the algorithm is that the learning process includes both the tasks of discovering a suitable network structure and of determining the connection weights. The entire network and its parameters are thought of evolved gradually in the learning process.
authors.affiliation: Digital Imaging and Computer Vision Laboratory, University of Nebraska at Omaha, Omaha, USA
authors.forename: Qiuming
authors.initials: Q
authors.lastname: Zhu
authors.forename: Yao
authors.initials: Y
authors.lastname: Cai
authors.forename: Luzheng
authors.initials: L
authors.lastname: Liu