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Multifactor Modelling with Regularization

  • Ventsislav Nikolov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9883)

Abstract

In this paper a multifactor modeling software system is described for building of a polynomial formula by a genetic algorithm. Thus a target variable is modeled by a subset of available explanatory variables represented as discrete time series. The proposed approach is improved by regularization in order to avoid the problem of overfitting.

Keywords

Multifactor Polynomial formula Basis functions Genetic algorithm Least squares regression Regularization 

References

  1. 1.
    Hamilton, J.: Time Series Analysis. Princeton University Press, Princeton (1994)Google Scholar
  2. 2.
    Koza, J.: Genetic Programming. MIT Press, Cambridge (1992)Google Scholar
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    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1999)Google Scholar
  4. 4.
    Rosen, K.: Discrete Mathematics and Its Applications, 4th edn. AT&T (1998)Google Scholar
  5. 5.
    Rosenberg, A.: Machine Learning Lectures, CUNY Graduate Center (2009). (http://eniac.cs.qc.cuny.edu/andrew/gcml/lecture5.pdf)

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.EuroRisk Systems Ltd.VarnaBulgaria

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