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Structural Risk Minimization

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  • First Online:
Encyclopedia of Machine Learning and Data Mining

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Abstract

Structural risk minimization is an inductive principle used to combat overfitting. It seeks a tradeoff between model complexity and fitness of the model on the training data.

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Recommended Reading

  • Vapnik V (1998) Statistical learning theory. John Wiley, New York

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Authors and Affiliations

  1. School of Computer Science, Australian National University, Canberra, ACT, Australia

    Xinhua Zhang

  2. NICTA London Circuit, Canberra, ACT, Australia

    Xinhua Zhang

Authors
  1. Xinhua Zhang
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Correspondence to Xinhua Zhang .

Editor information

Editors and Affiliations

  1. Engineering (CSE), University of New South Wales School of Computer Science &, Sydney, New South Wales, Australia

    Claude Sammut

  2. Software Engineering, Monash University School of Computer Science &, Melbourne, Victoria, Australia

    Geoffrey I. Webb

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© 2016 Springer Science+Business Media New York

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Zhang, X. (2016). Structural Risk Minimization. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_799-1

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  • DOI: https://doi.org/10.1007/978-1-4899-7502-7_799-1

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  • Publisher Name: Springer, Boston, MA

  • Online ISBN: 978-1-4899-7502-7

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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