KFU. Card publication. Non Linear Fitting Methods for Machine Learning

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

NON LINEAR FITTING METHODS FOR MACHINE LEARNING

Form of presentation

Conference proceedings in international journals and collections

Year of publication

2017

Язык

английский

Authors, employees of KFU

Magid Evgeniy Arkadevich, author

Bibliographic description in the original language

Martinez-Garcia E.A., Rodriguez N.A., Rodriguez-Jorge R., Mizera-Pietraszko J., Sheba J.K., Mohan R.E., Magid E. Non Linear Fitting Methods for Machine Learning // International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (Barcelona, Spain; 8-10 November 2017) - p. 807-818.

Annotation

International Conference on P2P, Parallel, Grid, Cloud and Internet Computing

Keywords

Machine learning

The name of the journal

International Conference on P2P, Parallel, Grid, Cloud and Internet Computing

URL

https://link.springer.com/chapter/10.1007/978-3-319-69835-9_76

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=257543&p_lang=2

Resource files

File name	Size (MB)	Format
F_2017_Non_Linear_Fitting_Methods_for_Machine_Learning.pdf	0,26	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Magid Evgeniy Arkadevich	ru_RU
dc.date.accessioned	2017-01-01T00:00:00Z	ru_RU
dc.date.available	2017-01-01T00:00:00Z	ru_RU
dc.date.issued	2017	ru_RU
dc.identifier.citation	Martinez-Garcia E.A., Rodriguez N.A., Rodriguez-Jorge R., Mizera-Pietraszko J., Sheba J.K., Mohan R.E., Magid E. Non Linear Fitting Methods for Machine Learning // International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (Barcelona, Spain; 8-10 November 2017) - p. 807-818.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=257543&p_lang=2	ru_RU
dc.description.abstract	International Conference on P2P, Parallel, Grid, Cloud and Internet Computing	ru_RU
dc.description.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 multidimension 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.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Non Linear Fitting Methods for Machine Learning	ru_RU
dc.type	Conference proceedings in international journals and collections	ru_RU