Paper Info
Title
Supervised Learning Using Local Analysis in an 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.
Year
DOI
Venue
2012
10.1109/SIBGRAPI.2012.53
Graphics, Patterns and Images
Keywords
Field
DocType
learning (artificial intelligence),multilayer perceptrons,pattern classification,support vector machines,MP,OPF classifier,OPF-LA,SVM,artificial neural networks,data classification,data training phase,local analysis,local information,multilayer perceptrons,optimal path forest -- local analysis,supervised learning,support vector machines,Optimal-Path Forest,Supervised classifiers
Heuristic,Computer science,Support vector machine,Feature extraction,Supervised learning,Artificial intelligence,Artificial neural network,Classifier (linguistics),Local analysis,Perceptron,Machine learning
Conference
ISSN
ISBN
Citations 
1530-1834
978-1-4673-2802-9
2
PageRank 
References 
Authors
0.39
5
2
Name
Order
Citations
PageRank
Willian Paraguassu Amorim120.39
Marcelo Henriques de Carvalho220.39