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Please use this identifier to cite or link to this item: https://rima.ufrrj.br/jspui/handle/20.500.14407/15771
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dc.contributor.authorTorres, Helainy Ignacio de Almeida-
dc.date.accessioned2024-01-23T12:05:36Z-
dc.date.available2024-01-23T12:05:36Z-
dc.date.issued2022-05-30-
dc.identifier.urihttps://rima.ufrrj.br/jspui/handle/20.500.14407/15771-
dc.description.abstractMachine learning (Aprendizado de Máquina) ganhou destaque por ser uma área da inteligên- cia artificial que permite que algoritmos tomem decisões através do conhecimento extraído de amostras de dados. Uma das áreas de Machine Learning são os algoritmos de classificação, que se baseiam em prever a classe de uma observação dada. Existem vários métodos na literatura, que resolvem problemas de classificação como Rede Neural, SVM, KNN entre outros. Uma das semelhanças entre eles é utilizar a métrica euclidiana para determinar erros e aproxima- ções. Nesse trabalho propomos construir um algoritmo baseado no KNN utilizando a métrica riemanniana para o problema de classificação de imagens. Os bancos de imagens utilizados durante a pesquisa são de imagens médicas e cada imagem será representada como uma matriz de covariância. O método proposto foi comparado com o KNN clássico que utiliza a métrica euclidiana e em todosos testes realizados se mostrou superior, apesar da qualidade das imagem, demonstrando que a técnica tem muito a oferecer.pt_BR
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESpt_BR
dc.languageporpt_BR
dc.publisherUniversidade Federal Rural do Rio de Janeiropt_BR
dc.subjectAprendizado de Máquinapt_BR
dc.subjectMétrica Riemannianapt_BR
dc.subjectKNNpt_BR
dc.subjectMachine Learningpt_BR
dc.subjectRiemannian Metricspt_BR
dc.titleUma proposta do algoritmo KNN sobre uma perspectiva riemanniana para o problema de classificação de imagenspt_BR
dc.title.alternativeA proposal of th KNN algorithm on a riemannian perspective for the image classification problemen
dc.typeDissertaçãopt_BR
dc.description.abstractOtherMachine learning has gained prominence as an area of artificial intelligence that allows algorithms to make decisions through knowledge extracted from data samples. One of the areas of Machine learning is classification algorithms, which are based on predicting the class of a given observation. There are several methods in the literature that solve classification problems such as Neural Network, SVM, KNN, among others. One of the similarities between them is to use the Euclidean metric to determine errors and approximations. In this work we propose to build an algorithm based on KNN using the Riemannian metric for the image classification problem. The image banks used during the research are of medical images and each image will be represented as a covariance matrix. The proposed method was compared with the classical KNN that uses the Euclidean metric and in all tests performed it proved to be superior, despite the image quality, demonstrating that the technique has a lot to offer.pt_BR
dc.contributor.advisor1Cruz, Marcelo Dib-
dc.contributor.advisor1ID016.628.007-03pt_BR
dc.contributor.advisor1IDhttps://orcid.org/0000-0002-0380-144Xpt_BR
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/7385995443437070pt_BR
dc.contributor.advisor-co1Gregório, Ronaldo Malheiros-
dc.contributor.advisor-co1ID077.117.167-61pt_BR
dc.contributor.advisor-co1IDhttps://orcid.org/0000-0003-2229-0523pt_BR
dc.contributor.advisor-co1Latteshttp://lattes.cnpq.br/4502104424266743pt_BR
dc.contributor.referee1Cruz, Marcelo Dib-
dc.contributor.referee1ID016.628.007-03pt_BR
dc.contributor.referee1IDhttps://orcid.org/0000-0002-0380-144Xpt_BR
dc.contributor.referee1Latteshttp://lattes.cnpq.br/7385995443437070pt_BR
dc.contributor.referee2Vera-Tudela, Carlos Andrés Reyna-
dc.contributor.referee2IDhttps://orcid.org/0000-0001-5855-8611pt_BR
dc.contributor.referee2Latteshttp://lattes.cnpq.br/6509989261742578pt_BR
dc.contributor.referee3França, Juliana Baptista dos Santos-
dc.contributor.referee3ID053.276.397-11pt_BR
dc.contributor.referee3Latteshttp://lattes.cnpq.br/9341068095520817pt_BR
dc.creator.ID092.252.057-75pt_BR
dc.creator.Latteshttp://lattes.cnpq.br/0381719929316735pt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentInstituto de Ciências Exataspt_BR
dc.publisher.initialsUFRRJpt_BR
dc.publisher.programPrograma de Pós-Graduação em Modelagem Matemática e Computacionalpt_BR
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dc.subject.cnpqMatemáticapt_BR
dc.subject.cnpqMatemáticapt_BR
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