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Reference TypeConference Paper (Conference Proceedings)
Last Update2012:
Metadata Last Update2020: administrator
Citation KeyAmorimCarv:2012:SuLeUs
TitleSupervised Learning Using Local Analysis in an Optimal-Path Forest
FormatDVD, On-line.
Access Date2021, Jan. 24
Number of Files1
Size609 KiB
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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
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:34 :: -> administrator :: 2012
2020-02-19 02:18:28 :: administrator -> :: 2012
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
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.
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