An iterative pruning method for second-order recurrent neural networks is presented. Each step consists in eliminating a unit and adjusting the remaining weights so that the network performance does not worsen over the training set. The pruning process involves solving a linear system of equations in the least-squares sense. The algorithm also provides a criterion for choosing the units to be removed, which works well in practice. Initial experimental results demonstrate the effectiveness of the proposed approach over high-order architectures.
Iterative pruning in second order recurrent neural networks
CASTELLANO, GIOVANNA;FANELLI, Anna Maria
1995-01-01
Abstract
An iterative pruning method for second-order recurrent neural networks is presented. Each step consists in eliminating a unit and adjusting the remaining weights so that the network performance does not worsen over the training set. The pruning process involves solving a linear system of equations in the least-squares sense. The algorithm also provides a criterion for choosing the units to be removed, which works well in practice. Initial experimental results demonstrate the effectiveness of the proposed approach over high-order architectures.File in questo prodotto:
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