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Type: Congresso
Title: Optimum-path Forest Based On K-connectivity: Theory And Applications
Author: Papa
Joao Paulo; Nachif Fernandes
Silas Evandro; Falcao
Alexandre Xavier
Abstract: Graph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the last years, mainly due to the promising results obtained by OPF-based classifiers, which range from unsupervised, semi-supervised and supervised learning. In this paper, we consider a deeper theoretical explanation concerning the supervised OPF classifier with k-neighborhood (OPFk), as well as we proposed two different training and classification algorithms that allow OPFk to work faster. The experimental validation against standard OPF and Support Vector Machines also validates the robustness of OPFk in real and synthetic datasets. (C) 2016 Elsevier B.V. All rights reserved.
Subject: Pattern Classification
Optimum-path Forest
Supervised Learning
Editor: Elsevier Science BV
Rights: fechado
Identifier DOI: 10.1016/j.patrec.2016.07.026
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

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