@InProceedings{AmorimCarv:2012:SuLeUs,
author = "Amorim, Willian Paraguassu and Carvalho, Marcelo Henriques de",
affiliation = "FACOM - Institute of Computing, Federal University of Mato Grosso
do Sul - UFMS and FACOM - Institute of Computing, Federal
University of Mato Grosso do Sul - UFMS",
title = "Supervised Learning Using Local Analysis in an Optimal-Path
Forest",
booktitle = "Proceedings...",
year = "2012",
editor = "Freitas, Carla Maria Dal Sasso and Sarkar, Sudeep and Scopigno,
Roberto and Silva, Luciano",
organization = "Conference on Graphics, Patterns and Images, 25. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
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.",
conference-location = "Ouro Preto, MG, Brazil",
conference-year = "22-25 Aug. 2012",
doi = "10.1109/SIBGRAPI.2012.53",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2012.53",
language = "en",
ibi = "8JMKD3MGPBW34M/3C9UQ2L",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3C9UQ2L",
targetfile = "PID2448677.pdf",
urlaccessdate = "2024, May 02"
}