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Iterative pruning in second-order recurrent neural networks

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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.

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Authors and Affiliations

  1. Dipartimento di Informatica, Universita degli Studi di Bari, Via Orabona 4, I-70126, Bari, Italy

    Giovanna Castellano, Anna Maria Fanelli & Marcello Pelillo

Authors
  1. Giovanna Castellano
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  2. Anna Maria Fanelli
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  3. Marcello Pelillo
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Castellano, G., Fanelli, A.M. & Pelillo, M. Iterative pruning in second-order recurrent neural networks. Neural Process Lett 2, 5–8 (1995). https://doi.org/10.1007/BF02309008

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  • DOI: https://doi.org/10.1007/BF02309008

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