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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M8SRRE
Repositorysid.inpe.br/sibgrapi/2016/08.12.00.56
Last Update2016:08.12.00.56.22 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/08.12.00.56.22
Metadata Last Update2022:05.18.22.21.07 (UTC) administrator
Citation KeyCamargoBugaSait:2016:AbApAt
TitleAbordagem de Aprendizado Ativo para Classificação de Dados Biomédicos
FormatOn-line
Year2016
Access Date2024, May 02
Number of Files1
Size130 KiB
2. Context
Author1 Camargo, Guilherme
2 Bugatti, Pedro Henrique
3 Saito, Priscila Tiemi Maeda
Affiliation1 Universidade Tecnológica Federal do Paraná (UTFPR)
2 Universidade Tecnológica Federal do Paraná (UTFPR)
3 Universidade Tecnológica Federal do Paraná (UTFPR) e Universidade Estadual de Campinas (UNICAMP)
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addressgcamargo@alunos.utfpr.edu.br
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeWork in Progress
History (UTC)2016-08-12 00:56:22 :: gcamargo@alunos.utfpr.edu.br -> administrator ::
2022-05-18 22:21:07 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsaprendizado ativo
análise de imagens
classificação
imagens biomédicas
floresta de caminhos ótimos
AbstractA huge volume of biomedical data (images, genes, among others) is daily generated. The analysis of such data is a complex task that demands specialized knowledge, and the level of expertise directly impacts the diagnosis. Besides, due to the volume of data such task becomes extremely tiresome, and hence highly susceptible to errors. Trying to solve this problem, machine learning approaches have been proposed in the literature to perform automatic classification of such data. Despite the several proposed techniques, the great majority strictly focus just on the effectiveness, and relegate the efficiency of the classification. This paper presents a novel learning approach capable to obtain high accuracies, as well as maintaining a minimal involvement of the expert and interactive computational time during the learning process. To do so, the proposed approach exploits the active learning paradigm, in order to reduce, organize and select the most informative samples to the learning process of the pattern classifier.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2016 > Abordagem de Aprendizado...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3M8SRRE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M8SRRE
Languagept
Target File2016-sibgrapi-wip.pdf
User Groupgcamargo@alunos.utfpr.edu.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 4
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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