Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/105797 https://doi.org/10.3390/app10061994 |
Resumo: | The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of abstract concepts by exploiting the geometrical view of concepts, without supervision. In the article, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT’s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with fiveMachine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size. |
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Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Conceptsunsupervised machine learninghierarchical learningcomputational representationcomputational cognitive modelingcontextual modelingclassificationIoT data modelingThe term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of abstract concepts by exploiting the geometrical view of concepts, without supervision. In the article, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT’s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with fiveMachine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size.MDPI2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/105797http://hdl.handle.net/10316/105797https://doi.org/10.3390/app10061994eng2076-3417Sharma, RahulRibeiro, BernardeteMiguel Pinto, AlexandreCardoso, F. Amílcarinfo: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:RCAAP2023-03-08T21:31:20Zoai:estudogeral.uc.pt:10316/105797Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:22:17.955469Repositório 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 |
Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts |
title |
Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts |
spellingShingle |
Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts Sharma, Rahul unsupervised machine learning hierarchical learning computational representation computational cognitive modeling contextual modeling classification IoT data modeling |
title_short |
Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts |
title_full |
Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts |
title_fullStr |
Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts |
title_full_unstemmed |
Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts |
title_sort |
Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts |
author |
Sharma, Rahul |
author_facet |
Sharma, Rahul Ribeiro, Bernardete Miguel Pinto, Alexandre Cardoso, F. Amílcar |
author_role |
author |
author2 |
Ribeiro, Bernardete Miguel Pinto, Alexandre Cardoso, F. Amílcar |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Sharma, Rahul Ribeiro, Bernardete Miguel Pinto, Alexandre Cardoso, F. Amílcar |
dc.subject.por.fl_str_mv |
unsupervised machine learning hierarchical learning computational representation computational cognitive modeling contextual modeling classification IoT data modeling |
topic |
unsupervised machine learning hierarchical learning computational representation computational cognitive modeling contextual modeling classification IoT data modeling |
description |
The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of abstract concepts by exploiting the geometrical view of concepts, without supervision. In the article, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT’s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with fiveMachine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/105797 http://hdl.handle.net/10316/105797 https://doi.org/10.3390/app10061994 |
url |
http://hdl.handle.net/10316/105797 https://doi.org/10.3390/app10061994 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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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