Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/81654
Author(s): João Paulo Papa
Willian Paraguassu Amorim
Alexandre Xavier Falcão
João Manuel R. S. Tavares
Title: Recent Advances on Optimum-Path Forest for Data Classification: Supervised, Semi-Supervised and Unsupervised Learning
Issue Date: 2016
Abstract: Although one can find several pattern recognition techniques out there, there is still room for improvements and new approaches. In this book chapter, we revisited the Optimum-Path Forest (OPF) classifier, which has been evaluated over the last years in a number of applications that consider supervised, semi-supervised and unsupervised learning problems. We also presented a brief compilation of a number of previous works that employed OPF in different research fields, that range from remote sensing image classification to medical data analysis.
Subject: Ciências Tecnológicas, Ciências da engenharia e tecnologias
Technological sciences, Engineering and technology
Scientific areas: Ciências da engenharia e tecnologias
Engineering and technology
URI: https://repositorio-aberto.up.pt/handle/10216/81654
Source: Handbook of Pattern Recognition and Computer Vision
Document Type: Capítulo ou Parte de Livro
Rights: openAccess
License: https://creativecommons.org/licenses/by-nc/4.0/
Appears in Collections:FEUP - Capítulo ou Parte de Livro

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