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Evidence Maximization Technique for Training of Elastic Nets

Author

Listed:
  • Igor Dubnov
  • Alexander Merkov
  • Vladimir Arlazarov
  • Ilia Nikolaev

Abstract

This paper presents a technique of evidence maximization for automatic tuning of regularization parameters of elastic nets, which allows tuning many parameters simultaneously. This technique was applied to handwritten digit recognition. Experiments showed its ability to train either models with high accuracy of recognition or highly sparse models with reasonable accuracy.

Suggested Citation

  • Igor Dubnov & Alexander Merkov & Vladimir Arlazarov & Ilia Nikolaev, 2016. "Evidence Maximization Technique for Training of Elastic Nets," Journal of Optimization, Hindawi, vol. 2016, pages 1-7, June.
    • Handle: RePEc:hin:jjopti:2659012
    DOI: 10.1155/2016/2659012
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    References listed on IDEAS

    as
    1. NESTEROV, Yu., 2007. "Gradient methods for minimizing composite objective function," LIDAM Discussion Papers CORE 2007076, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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