Fine-tuning artificial neural networks automatically
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Outros Autores: | , , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/67390 |
Resumo: | To get the most out of powerful tools expert knowledge is often required. Experts are the ones with the suitable knowledge to tune the tools parameters. In this paper we assess several techniques which can automatically fine tune ANN parameters. Those techniques include the use of GA and Stratified Sampling. The tuning includes the choice of the best ANN structure and the best network biases and their weights. Empirical results achieved in experiments performed using nine heterogeneous data sets show that the use of the proposed Stratified Sampling technique is advantageous. |
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Fine-tuning artificial neural networks automaticallyEngenharia do conhecimento, Engenharia electrotécnica, electrónica e informáticaKnowledge engineering, Electrical engineering, Electronic engineering, Information engineeringTo get the most out of powerful tools expert knowledge is often required. Experts are the ones with the suitable knowledge to tune the tools parameters. In this paper we assess several techniques which can automatically fine tune ANN parameters. Those techniques include the use of GA and Stratified Sampling. The tuning includes the choice of the best ANN structure and the best network biases and their weights. Empirical results achieved in experiments performed using nine heterogeneous data sets show that the use of the proposed Stratified Sampling technique is advantageous.20072007-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/67390eng10.1007/978-0-387-84814-3_5Francisco ReinaldoRui CamachoLuís P. ReisDemétrio Renó Magalhãesinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T15:25:40Zoai:repositorio-aberto.up.pt:10216/67390Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:23:27.306815Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Fine-tuning artificial neural networks automatically |
title |
Fine-tuning artificial neural networks automatically |
spellingShingle |
Fine-tuning artificial neural networks automatically Francisco Reinaldo Engenharia do conhecimento, Engenharia electrotécnica, electrónica e informática Knowledge engineering, Electrical engineering, Electronic engineering, Information engineering |
title_short |
Fine-tuning artificial neural networks automatically |
title_full |
Fine-tuning artificial neural networks automatically |
title_fullStr |
Fine-tuning artificial neural networks automatically |
title_full_unstemmed |
Fine-tuning artificial neural networks automatically |
title_sort |
Fine-tuning artificial neural networks automatically |
author |
Francisco Reinaldo |
author_facet |
Francisco Reinaldo Rui Camacho Luís P. Reis Demétrio Renó Magalhães |
author_role |
author |
author2 |
Rui Camacho Luís P. Reis Demétrio Renó Magalhães |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Francisco Reinaldo Rui Camacho Luís P. Reis Demétrio Renó Magalhães |
dc.subject.por.fl_str_mv |
Engenharia do conhecimento, Engenharia electrotécnica, electrónica e informática Knowledge engineering, Electrical engineering, Electronic engineering, Information engineering |
topic |
Engenharia do conhecimento, Engenharia electrotécnica, electrónica e informática Knowledge engineering, Electrical engineering, Electronic engineering, Information engineering |
description |
To get the most out of powerful tools expert knowledge is often required. Experts are the ones with the suitable knowledge to tune the tools parameters. In this paper we assess several techniques which can automatically fine tune ANN parameters. Those techniques include the use of GA and Stratified Sampling. The tuning includes the choice of the best ANN structure and the best network biases and their weights. Empirical results achieved in experiments performed using nine heterogeneous data sets show that the use of the proposed Stratified Sampling technique is advantageous. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007 2007-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/67390 |
url |
https://hdl.handle.net/10216/67390 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1007/978-0-387-84814-3_5 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136150474981376 |