Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patients

Detalhes bibliográficos
Autor(a) principal: Kazak, Veronica
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/71589
Resumo: Dissertation presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business Intelligence
id RCAP_74975d3c241ee5bc90ac221abf2f4660
oai_identifier_str oai:run.unl.pt:10362/71589
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patientsAutoencoderRepresentation LearningFeature ExtractionUnsupervised LearningDeep LearningDissertation presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business IntelligenceData representation is one of the fundamental concepts in machine learning. An appropriate representation is found by discovering a structure and automatic detection of patterns in data. In many domains, representation or feature learning is a critical step in improving the performance of machine learning algorithms due to the multidimensionality of data that feeds the model. Some tasks may have different perspectives and approaches depending on how data is represented. In recent years, deep artificial neural networks have provided better solutions to several pattern recognition problems and classification tasks. Deep architectures have also shown their effectiveness in capturing latent features for data representation. In this document, autoencoders will be examined to obtain the representation of Parkinson's disease patients and compared with conventional representation learning algorithms. The results will show whether the proposed method of feature selection leads to the desired accuracy for predicting the severity of Parkinson’s disease.Henriques, Roberto André PereiraCastelli, MauroRUNKazak, Veronica2019-06-03T16:28:31Z2019-04-032019-04-03T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/71589TID:202250776enginfo: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:RCAAP2024-03-11T04:33:40Zoai:run.unl.pt:10362/71589Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:35:12.946786Repositório 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 Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patients
title Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patients
spellingShingle Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patients
Kazak, Veronica
Autoencoder
Representation Learning
Feature Extraction
Unsupervised Learning
Deep Learning
title_short Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patients
title_full Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patients
title_fullStr Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patients
title_full_unstemmed Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patients
title_sort Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patients
author Kazak, Veronica
author_facet Kazak, Veronica
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Kazak, Veronica
dc.subject.por.fl_str_mv Autoencoder
Representation Learning
Feature Extraction
Unsupervised Learning
Deep Learning
topic Autoencoder
Representation Learning
Feature Extraction
Unsupervised Learning
Deep Learning
description Dissertation presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business Intelligence
publishDate 2019
dc.date.none.fl_str_mv 2019-06-03T16:28:31Z
2019-04-03
2019-04-03T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/71589
TID:202250776
url http://hdl.handle.net/10362/71589
identifier_str_mv TID:202250776
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: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
_version_ 1799137973281751040

Registros relacionados