title: The ensemble approach to neural-network learning and generalization.
abstract: In this paper a new method is suggested for learning and generalization with a general one-hidden layer feedforward neural network. This scheme encompasses the use of a linear combination of heterogeneous nodes having randomly prescribed parameter values. The learning of the parameters is realized through adaptive stochastic optimization using a generalization data set. The learning of the linear coefficients in the linear combination of nodes is achieved with a linear regression method using data from the training set. One node is learned at a time. The method allows for choosing the proper number of net nodes, and is computationally efficient. The method was tested on mathematical examples and real problems from materials science and technology.
authors.affiliation: Case Western Reserve University, Cleveland, OH 44106, USA.
authors.forename: B
authors.initials: B
authors.lastname: Igelnik
authors.forename: Y H
authors.initials: YH
authors.lastname: Pao
authors.forename: S R
authors.initials: SR
authors.lastname: LeClair
authors.forename: C Y
authors.initials: CY
authors.lastname: Shen