FEMaR: A finite element machine for regression problems
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IJCNN.2017.7966195 http://hdl.handle.net/11449/232659 |
Resumo: | Regression-based tasks have been the forerunner regarding the application of machine learning tools in the context of data mining. Problems related to price and stock prediction, selling estimation, and weather forecasting are commonly used as benchmarking for the comparison of regression techniques, just to name a few. Neural Networks, Decision Trees and Support Vector Machines are the most widely used approaches concerning regression-oriented applications, since they can generalize well in a number of different applications. In this work, we propose an efficient and effective regression technique based on the Finite Element Method (FEM) theory, hereinafter called Finite Element Machine for Regression (FEMaR). The proposed approach has only one parameter and it has a quadratic complexity for both training and classification phases when we use basis functions that obey some properties, as well as we show the proposed approach can obtain very competitive results when compared against some state-of-the-art regression techniques. |
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Repositório Institucional da UNESP |
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FEMaR: A finite element machine for regression problemsRegression-based tasks have been the forerunner regarding the application of machine learning tools in the context of data mining. Problems related to price and stock prediction, selling estimation, and weather forecasting are commonly used as benchmarking for the comparison of regression techniques, just to name a few. Neural Networks, Decision Trees and Support Vector Machines are the most widely used approaches concerning regression-oriented applications, since they can generalize well in a number of different applications. In this work, we propose an efficient and effective regression technique based on the Finite Element Method (FEM) theory, hereinafter called Finite Element Machine for Regression (FEMaR). The proposed approach has only one parameter and it has a quadratic complexity for both training and classification phases when we use basis functions that obey some properties, as well as we show the proposed approach can obtain very competitive results when compared against some state-of-the-art regression techniques.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Computing São Paulo State UniversityDepartment of Electrical Engineering São Paulo State UniversityDepartment of Computing São Paulo State UniversityDepartment of Electrical Engineering São Paulo State UniversityFAPESP: 2013/07375- 0FAPESP: 2014/12236-1FAPESP: 2014/16250-9CNPq: 306166/2014-3Universidade Estadual Paulista (UNESP)Pereira, Danillo R. [UNESP]Papa, Joao P. [UNESP]Souza, Andre N. [UNESP]2022-04-30T02:38:56Z2022-04-30T02:38:56Z2017-06-30info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2751-2757http://dx.doi.org/10.1109/IJCNN.2017.7966195Proceedings of the International Joint Conference on Neural Networks, v. 2017-May, p. 2751-2757.http://hdl.handle.net/11449/23265910.1109/IJCNN.2017.79661952-s2.0-85031015868Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/232659Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
FEMaR: A finite element machine for regression problems |
title |
FEMaR: A finite element machine for regression problems |
spellingShingle |
FEMaR: A finite element machine for regression problems Pereira, Danillo R. [UNESP] |
title_short |
FEMaR: A finite element machine for regression problems |
title_full |
FEMaR: A finite element machine for regression problems |
title_fullStr |
FEMaR: A finite element machine for regression problems |
title_full_unstemmed |
FEMaR: A finite element machine for regression problems |
title_sort |
FEMaR: A finite element machine for regression problems |
author |
Pereira, Danillo R. [UNESP] |
author_facet |
Pereira, Danillo R. [UNESP] Papa, Joao P. [UNESP] Souza, Andre N. [UNESP] |
author_role |
author |
author2 |
Papa, Joao P. [UNESP] Souza, Andre N. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Pereira, Danillo R. [UNESP] Papa, Joao P. [UNESP] Souza, Andre N. [UNESP] |
description |
Regression-based tasks have been the forerunner regarding the application of machine learning tools in the context of data mining. Problems related to price and stock prediction, selling estimation, and weather forecasting are commonly used as benchmarking for the comparison of regression techniques, just to name a few. Neural Networks, Decision Trees and Support Vector Machines are the most widely used approaches concerning regression-oriented applications, since they can generalize well in a number of different applications. In this work, we propose an efficient and effective regression technique based on the Finite Element Method (FEM) theory, hereinafter called Finite Element Machine for Regression (FEMaR). The proposed approach has only one parameter and it has a quadratic complexity for both training and classification phases when we use basis functions that obey some properties, as well as we show the proposed approach can obtain very competitive results when compared against some state-of-the-art regression techniques. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-06-30 2022-04-30T02:38:56Z 2022-04-30T02:38:56Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/IJCNN.2017.7966195 Proceedings of the International Joint Conference on Neural Networks, v. 2017-May, p. 2751-2757. http://hdl.handle.net/11449/232659 10.1109/IJCNN.2017.7966195 2-s2.0-85031015868 |
url |
http://dx.doi.org/10.1109/IJCNN.2017.7966195 http://hdl.handle.net/11449/232659 |
identifier_str_mv |
Proceedings of the International Joint Conference on Neural Networks, v. 2017-May, p. 2751-2757. 10.1109/IJCNN.2017.7966195 2-s2.0-85031015868 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Joint Conference on Neural Networks |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
2751-2757 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799965616763830272 |