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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3C9UQ2L
Repositorysid.inpe.br/sibgrapi/2012/07.15.22.51
Last Update2012:07.15.22.51.27 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2012/07.15.22.51.27
Metadata Last Update2022:06.14.00.07.36 (UTC) administrator
DOI10.1109/SIBGRAPI.2012.53
Citation KeyAmorimCarv:2012:SuLeUs
TitleSupervised Learning Using Local Analysis in an Optimal-Path Forest
FormatDVD, On-line.
Year2012
Access Date2025, July 15
Number of Files1
Size609 KiB
2. Context
Author1 Amorim, Willian Paraguassu
2 Carvalho, Marcelo Henriques de
Affiliation1 FACOM - Institute of Computing, Federal University of Mato Grosso do Sul - UFMS
2 FACOM - Institute of Computing, Federal University of Mato Grosso do Sul - UFMS
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
e-Mail Addressparaguassuec@gmail.com
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto, MG, Brazil
Date22-25 Aug. 2012
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2012-09-20 16:45:34 :: paraguassuec@gmail.com -> administrator :: 2012
2022-03-08 21:03:24 :: administrator -> menottid@gmail.com :: 2012
2022-03-10 12:49:08 :: menottid@gmail.com -> administrator :: 2012
2022-06-14 00:07:36 :: administrator -> :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsSupervised classifiers
Optimal-Path Forest
AbstractIn this paper, we present an OPF-LA (Optimal Path Forest--Local Analysis), a new learning model proposal. OPF-LA is a heuristic that uses local information for selecting prototypes that, in turn, will be used to classify new data. It employs the main ideas of an OPF classifier, suggesting a new procedure in the data training phase. Experimental results show the advantages in efficiency and accuracy over classical learning algorithms in areas such as Support Vector Machines (SVM), Artificial Neural Networks using Multilayer Perceptrons (MP), and Optimal Path Forest (OPF), in several applications.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2012 > Supervised Learning Using...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Supervised Learning Using...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 15/07/2012 19:51 0.7 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3C9UQ2L
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3C9UQ2L
Languageen
Target FilePID2448677.pdf
User Groupparaguassuec@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SL8GS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.03.31 38
sid.inpe.br/sibgrapi/2022/06.10.21.49 8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage 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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