The ensemble approach to neural-network learning and generalization
B. Igelnik, Yoh‐Han Pao, Steven R. LeClair, Chang Yun Shen · IEEE Transactions on Neural Networks · 1999
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.