Fast Generation of a Sequence of Trained and Validated Feed-Forward Networks

Pramod L. Narasimha, Walter Delashmit, Michael Manry, Jiang Li, Francisco Maldonado

In this paper, three approaches are presented for generating and validating sequences of different size neural nets. First, a growing method is given along with several weight initialization methods, and their properties. Then a one pass pruning method is presented which utilizes orthogonal least squares. Based upon this pruning approach, a one-pass validation method is discussed. Finally, a training method that combines growing and pruning is described. In several examples, it is shown that the combination approach is superior to growing or pruning alone.

Subjects: 14. Neural Networks; 12. Machine Learning and Discovery

Submitted: Feb 10, 2006

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