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: 2015
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. (c) 2016 by World Scientific Publishing Co. Pte. Ltd.
Subject: Ciências Tecnológicas, Ciências da engenharia e tecnologias
Technological sciences, Engineering and technology
Scientific areas: Ciências da engenharia e tecnologias
Engineering and technology
DOI: 10.1142/9789814656535_0006
URI: https://hdl.handle.net/10216/81654
Source: Handbook Of Pattern Recognition And Computer Vision (5th Edition)
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

Files in This Item:
File Description SizeFormat 
107756.pdfChapter727.56 kBAdobe PDFThumbnail
View/Open


This item is licensed under a Creative Commons License Creative Commons