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Non Linear Fitting Methods for Machine Learning

  • Edgar A. Martínez-GarcíaEmail author
  • Nancy Ávila Rodríguez
  • Ricardo Rodríguez-Jorge
  • Jolanta Mizera-Pietraszko
  • Jaichandar Kulandaidaasan Sheba
  • Rajesh Elara Mohan
  • Evgeni Magid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 13)

Abstract

This manuscript presents an analysis of numerical fitting methods used for solving classification problems as discriminant functions in machine learning. Non linear polynomial, exponential, and trigonometric models are mathematically deduced and discussed. Analysis about their pros and cons, and their mathematical modelling are made on what method to chose for what type of highly non linear multi-dimension problems are more suitable to be solved. In this study only deterministic models with analytic solutions are involved, or parameters calculation by numeric methods, which the complete model can subsequently be treated as a theoretical model. Models deduction are summarised and presented as a survey.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Edgar A. Martínez-García
    • 1
    Email author
  • Nancy Ávila Rodríguez
    • 2
  • Ricardo Rodríguez-Jorge
    • 1
  • Jolanta Mizera-Pietraszko
    • 3
  • Jaichandar Kulandaidaasan Sheba
    • 4
  • Rajesh Elara Mohan
    • 5
  • Evgeni Magid
    • 6
  1. 1.Universidad Autónoma de Ciudad JuárezCiudad JuárezMexico
  2. 2.University of Texas at El PasoEl PasoUSA
  3. 3.Opole UniversityOpolePoland
  4. 4.Singapore PolytechnicSingaporeSingapore
  5. 5.Singapore University of Technology and DesignSingaporeSingapore
  6. 6.Kazan Federal UniversityKazanRussian Federation

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