6533b853fe1ef96bd12acd02

RESEARCH PRODUCT

Non Linear Fitting Methods for Machine Learning

Jaichandar Kulandaidaasan ShebaJolanta Mizera-pietraszkoEdgar A. Martinez-garciaRajesh Elara MohanNancy ÁVila RodríguezRicardo Rodríguez-jorgeEvgeni Magid

subject

PolynomialWake-sleep algorithmbusiness.industryComputer scienceOnline machine learningType (model theory)Machine learningcomputer.software_genreExponential functionNonlinear systemDiscriminantArtificial intelligenceTrigonometrybusinesscomputer

description

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.

yearjournalcountryeditionlanguage
2017-11-03
https://doi.org/10.1007/978-3-319-69835-9_76