Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPBW34M/3C9UQ2L |
Repository | sid.inpe.br/sibgrapi/2012/07.15.22.51 |
Last Update | 2012:07.15.22.51.27 paraguassuec@gmail.com |
Metadata | sid.inpe.br/sibgrapi/2012/07.15.22.51.27 |
Metadata Last Update | 2020:02.19.02.18.28 administrator |
Citation Key | AmorimCarv:2012:SuLeUs |
Title | Supervised Learning Using Local Analysis in an Optimal-Path Forest  |
Format | DVD, On-line. |
Year | 2012 |
Access Date | 2021, Jan. 24 |
Number of Files | 1 |
Size | 609 KiB |
Context area | |
Author | 1 Amorim, Willian Paraguassu 2 Carvalho, Marcelo Henriques de |
Affiliation | 1 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 |
Editor | Freitas, Carla Maria Dal Sasso Sarkar, Sudeep Scopigno, Roberto Silva, Luciano |
e-Mail Address | paraguassuec@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 25 (SIBGRAPI) |
Conference Location | Ouro Preto |
Date | Aug. 22-25, 2012 |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2012-09-20 16:45:34 :: paraguassuec@gmail.com -> administrator :: 2012 2020-02-19 02:18:28 :: administrator -> :: 2012 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | Supervised classifiers, Optimal-Path Forest. |
Abstract | In 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. |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPBW34M/3C9UQ2L |
zipped data URL | http://urlib.net/zip/8JMKD3MGPBW34M/3C9UQ2L |
Language | en |
Target File | PID2448677.pdf |
User Group | paraguassuec@gmail.com |
Visibility | shown |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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