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Data compression and prediction in neural networks

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  • Meir, Ronny
  • Fontanari, Jose F.

Abstract

We study the relationship between data compression and prediction in single-layer neural networks of limited complexity. Quantifying the intuitive notion of Occam's razor using Rissanen's minimum complexity framework, we investigate the model-selection criterion advocated by this principle. While we find that the criterion works well for large sample sizes (as it must for consistency), the behavior for finite sample sizes is rather complex, depending intricately on the relationship between the complexity of the hypothesis space and the target space. We also show that the limited networks studied perform efficient data compression, even in the error full regime.

Suggested Citation

  • Meir, Ronny & Fontanari, Jose F., 1993. "Data compression and prediction in neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 200(1), pages 644-654.
    • Handle: RePEc:eee:phsmap:v:200:y:1993:i:1:p:644-654
    DOI: 10.1016/0378-4371(93)90571-K
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    Cited by:

    1. Chapeau-Blondeau, François & Rousseau, David, 2009. "The minimum description length principle for probability density estimation by regular histograms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(18), pages 3969-3984.

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