Universal Genetic Programming: a Meta Learning Approach based on Semantics

Detalhes bibliográficos
Autor(a) principal: Re, Alessandro
Data de Publicação: 2019
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/79664
Resumo: A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision Systems
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spelling Universal Genetic Programming: a Meta Learning Approach based on SemanticsUniversalGeneticProgrammingMeta learningSemanticsA thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsWith the advancements of Machine Learning, the number of predictive models that can be used in a given situation has grown incredibly, and scientists willing to use Machine Learning have to spend a significant amount of time in searching, testing and tuning those models. This has an inevitable impact on the research quality. Many scientists are currently working on different approaches to automate this process by devising algorithms that can tune, select or combine multiple models for a specific application. This is the case of ensemble methods, hyper-heuristics and meta-learning algorithms. There have been great progresses in this direction, but typical approaches lack the presence of an unifying structure onto which these ensemble, hyper or meta algorithms are developed. In this thesis we discuss about a new meta-learning method based on Geometric Semantic Genetic Programming. The milestone introduced by this approach is the use of semantics as an intermediate representation to work with models of different nature. We will see how this approach is general and can be applied with any model, in particular we will apply this case to regression problems and we will test our hypotheses by experimental verification over some datasets for real-life problems.Castelli, MauroVanneschi, LeonardoRUNRe, Alessandro2020-07-23T00:30:42Z2019-07-232019-07-23T00:00:00Zdoctoral thesisinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10362/79664TID:101599145enginfo: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:RCAAP2024-05-22T17:40:40Zoai:run.unl.pt:10362/79664Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:11:59.867058Repositórios 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 Universal Genetic Programming: a Meta Learning Approach based on Semantics
title Universal Genetic Programming: a Meta Learning Approach based on Semantics
spellingShingle Universal Genetic Programming: a Meta Learning Approach based on Semantics
Re, Alessandro
Universal
Genetic
Programming
Meta learning
Semantics
title_short Universal Genetic Programming: a Meta Learning Approach based on Semantics
title_full Universal Genetic Programming: a Meta Learning Approach based on Semantics
title_fullStr Universal Genetic Programming: a Meta Learning Approach based on Semantics
title_full_unstemmed Universal Genetic Programming: a Meta Learning Approach based on Semantics
title_sort Universal Genetic Programming: a Meta Learning Approach based on Semantics
author Re, Alessandro
author_facet Re, Alessandro
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Re, Alessandro
dc.subject.por.fl_str_mv Universal
Genetic
Programming
Meta learning
Semantics
topic Universal
Genetic
Programming
Meta learning
Semantics
description A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision Systems
publishDate 2019
dc.date.none.fl_str_mv 2019-07-23
2019-07-23T00:00:00Z
2020-07-23T00:30:42Z
dc.type.driver.fl_str_mv doctoral thesis
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/79664
TID:101599145
url http://hdl.handle.net/10362/79664
identifier_str_mv TID:101599145
dc.language.iso.fl_str_mv eng
language eng
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
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
repository.mail.fl_str_mv info@rcaap.pt
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