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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RNN9JH
Repositorysid.inpe.br/sibgrapi/2018/08.31.09.43
Last Update2018:08.31.09.43.59 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/08.31.09.43.59
Metadata Last Update2022:06.14.00.09.14 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00057
Citation KeyBenatoTeleFalc:2018:SeLeIn
TitleSemi-Supervised Learning with Interactive Label Propagation guided by Feature Space Projections
FormatOn-line
Year2018
Access Date2024, Apr. 28
Number of Files1
Size2297 KiB
2. Context
Author1 Benato, Bárbara Caroline
2 Telea, Alexandru Cristian
3 Falcão, Alexandre Xavier
Affiliation1 University of Campinas
2 University of Groningen
3 University of Campinas
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressbarbarabenato@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-08-31 09:43:59 :: barbarabenato@gmail.com -> administrator ::
2022-06-14 00:09:14 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsSemi-Supervised Learning
Interactive Label Propagation
Auto-Encoder Neural Networks
Visual Analytics
AbstractWhile the number of unsupervised samples for data annotation is usually high, the absence of large supervised train- ing sets for effective feature learning and design of high-quality classifiers is a known problem whenever specialists are required for data supervision. By exploring the feature space of supervised and unsupervised samples, semi-supervised learning approaches can usually improve the classification system. However, these approaches do not usually exploit the pattern-finding power of the users visual system during machine learning. In this paper, we incorporate the user in the semi-supervised learning process by letting the feature space projection of unsupervised and supervised samples guide the label propagation actions of the user to the unsupervised samples. We show that this procedure can significantly reduce user effort while improving the quality of the classifier on unseen test sets. Due to the limited number of supervised samples, we also propose the use of auto-encoder neural networks for feature learning. For validation, we compare the classifiers that result from the proposed approach with the ones trained from the supervised samples only and semi-supervised trained using automatic label propagation.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > Semi-Supervised Learning with...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Semi-Supervised Learning with...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RNN9JH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RNN9JH
Languageen
Target FilePID5546009.pdf
User Groupbarbarabenato@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 9
sid.inpe.br/sibgrapi/2022/06.10.21.49 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 volume


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