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 |
| 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 | Size | Format | |
|---|---|---|---|---|
| 107756.pdf | Chapter | 727.56 kB | Adobe PDF | ![]() View/Open |
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