Results 51 to 60 of about 5,516,106 (266)
RFDCR:Automated brain lesion segmentation using cascaded random forests with dense conditional random fields [PDF]
, 2020 Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy.
Chen, Gaoxiang +5 more
core +2 more sources
Aggregated Recommendation through Random Forests
The Scientific World Journal, 2014 Aggregated recommendation refers to the process of suggesting one kind of items to a group of users. Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation.
Heng-Ru Zhang, Fan Min, Xu He
doaj +1 more source
Conditional variable importance for random forests
BMC Bioinformatics, 2008 Background Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables.
Augustin Thomas +4 more
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The Annals of Applied Statistics, 2008 Published in at http://dx.doi.org/10.1214/08-AOAS169 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Ishwaran, Hemant +3 more
openaire +4 more sources
Journal of Applied Mathematics, 2012 Random forests are known to be good for data mining of classification tasks, because random forests are robust for datasets having insufficient information possibly with some errors. But applying random forests blindly may not produce good results, and a
Hyontai Sug
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Latin American Journal of Probability and Mathematical Statistics, 2018 Let $F(N,m)$ denote a random forest on a set of $N$ vertices, chosen uniformly from all forests with $m$ edges. Let $F(N,p)$ denote the forest obtained by conditioning the Erdos-Renyi graph $G(N,p)$ to be acyclic. We describe scaling limits for the largest components of $F(N,p)$ and $F(N,m)$, in the critical window $p=N^{-1}+O(N^{-4/3})$ or $m=N/2+O(N^{
Martin, JB, Yeo, D
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RANDOM FORESTS-BASED FEATURE SELECTION FOR LAND-USE CLASSIFICATION USING LIDAR DATA AND ORTHOIMAGERY [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The development of lidar system, especially incorporated with high-resolution camera components, has shown great potential for urban classification.
H. Guan, J. Yu, J. Li, J. Li, L. Luo
doaj +1 more source
Water, 2019 Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm ...
Hristos Tyralis +2 more
semanticscholar +1 more source
Cooperative Profit Random Forests With Application in Ocean Front Recognition
IEEE Access, 2017 Random Forests are powerful classification and regression tools that are commonly applied in machine learning and image processing. In the majority of random classification forests algorithms, the Gini index and the information gain ratio are commonly ...
Jianyuan Sun +4 more
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