Explorations of the Semantic Learning Machine Neuroevolution Algorithm

Detalhes bibliográficos
Autor(a) principal: Gonçalves, Ivo
Data de Publicação: 2020
Outros Autores: Seca, Marta, Castelli, Mauro
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/142672
Resumo: Gonçalves, I., Seca, M., & Castelli, M. (2020). Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives. In W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, & B. Worzel (Eds.), Genetic Programming Theory and Practice XVII: Genetic and Evolutionary Computation (pp. 39-62). [Chapter 3] (Genetic Programming Theory and Practice XVII). Springer. https://doi.org/10.1007/978-3-030-39958-0_3
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spelling Explorations of the Semantic Learning Machine Neuroevolution AlgorithmDynamic Training Data Use, Ensemble Construction Methods, and Deep Learning PerspectivesGonçalves, I., Seca, M., & Castelli, M. (2020). Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives. In W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, & B. Worzel (Eds.), Genetic Programming Theory and Practice XVII: Genetic and Evolutionary Computation (pp. 39-62). [Chapter 3] (Genetic Programming Theory and Practice XVII). Springer. https://doi.org/10.1007/978-3-030-39958-0_3The recently proposed Semantic Learning Machine (SLM) neuroevolution algorithm is able to construct Neural Networks (NNs) over unimodal error landscapes in any supervised learning problem where the error is measured as a distance to the known targets. This chapter studies how different methods of dynamically using the training data affect the resulting generalization of the SLM algorithm. Across four real-world binary classification datasets, SLM is shown to outperform the Multi-layer Perceptron, with statistical significance, after parameter tuning is performed in both algorithms. Furthermore, this chapter also studies how different ensemble constructions methods influence the resulting generalization. The results show that the stochastic nature of SLM already confers enough diversity to the ensembles such that Bagging and Boosting cannot improve upon a simple averaging ensemble construction method. Finally, some initial results with SLM and Convolutional NNs are presented and future Deep Learning perspectives are discussed.SpringerNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNGonçalves, IvoSeca, MartaCastelli, Mauro2022-07-29T22:15:15Z2020-05-082020-05-08T00:00:00Zbook partinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10362/142672eng978-3-030-39957-31932-0167PURE: 27629624https://doi.org/10.1007/978-3-030-39958-0_3info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:04:08Zoai:run.unl.pt:10362/142672Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:34:41.981327Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Explorations of the Semantic Learning Machine Neuroevolution Algorithm
Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives
title Explorations of the Semantic Learning Machine Neuroevolution Algorithm
spellingShingle Explorations of the Semantic Learning Machine Neuroevolution Algorithm
Gonçalves, Ivo
title_short Explorations of the Semantic Learning Machine Neuroevolution Algorithm
title_full Explorations of the Semantic Learning Machine Neuroevolution Algorithm
title_fullStr Explorations of the Semantic Learning Machine Neuroevolution Algorithm
title_full_unstemmed Explorations of the Semantic Learning Machine Neuroevolution Algorithm
title_sort Explorations of the Semantic Learning Machine Neuroevolution Algorithm
author Gonçalves, Ivo
author_facet Gonçalves, Ivo
Seca, Marta
Castelli, Mauro
author_role author
author2 Seca, Marta
Castelli, Mauro
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Gonçalves, Ivo
Seca, Marta
Castelli, Mauro
description Gonçalves, I., Seca, M., & Castelli, M. (2020). Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives. In W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, & B. Worzel (Eds.), Genetic Programming Theory and Practice XVII: Genetic and Evolutionary Computation (pp. 39-62). [Chapter 3] (Genetic Programming Theory and Practice XVII). Springer. https://doi.org/10.1007/978-3-030-39958-0_3
publishDate 2020
dc.date.none.fl_str_mv 2020-05-08
2020-05-08T00:00:00Z
2022-07-29T22:15:15Z
dc.type.driver.fl_str_mv book part
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/142672
url http://hdl.handle.net/10362/142672
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-030-39957-3
1932-0167
PURE: 27629624
https://doi.org/10.1007/978-3-030-39958-0_3
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.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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