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A Markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach
(2011-11-28)
The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed ...
Modeling forest biomass using Very-High-Resolution data - Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images
(Italian Society of Remote Sensing, 2015)
We used spectral, textural and photogrammetric information from very-high resolution (VHR) stereo satellite data (Pleiades and WorldView-2) to estimate forest biomass across two test sites located in Chile and Germany. We ...
Climate-driven statistical models as effective predictors of local dengue incidence in costa rica: a generalized additive model and random forest approach
(Centro de Investigaciones en Matemática Pura y Aplicada (CIMPA) y Escuela de Matemática, San José, Costa Rica., 2020)
Modeling forest biomass using Very-High-Resolution data - Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images
(Italian Society of Remote Sensing, 2015)
On the Influence of Markovian Models for Contextual-Based Optimum-Path Forest Classification
(Springer, 2014-01-01)
Contextual classification considers the information about a sample's neighborhood to improve standard pixel-based classification approaches. In this work, we evaluated four different Markovian models for Optimum-Path Forest ...
On the influence of Markovian models for contextual-based Optimum-Path Forest classification
(2014-01-01)
Contextual classification considers the information about a sample’s neighborhood to improve standard pixel-based classification approaches. In this work, we evaluated four different Markovian models for Optimum-Path Forest ...
Influence of random forest hyperparameterization on short-term runoff forecasting in an andean mountain catchment
(2021)
The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations ...