Results 61 to 70 of about 6,745,293 (217)
, 2019 Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursively cutting a domain. The flexibility of these methods is often limited by the requirement that the cuts be axis aligned.
Ge, S +4 more
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COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm
Frontiers in Public Health, 2020 Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature.
C. Iwendi +8 more
semanticscholar +1 more source
Multimodal random forest based tensor regression
IET Computer Vision, 2014 This study presents a method, called random forest based tensor regression, for real‐time head pose estimation using both depth and intensity data. The method builds on random forests and proposes to train and use tensor regressors at each leaf node of ...
Sertan Kaymak, Ioannis Patras
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Random forest-based track initiation method
The Journal of Engineering, 2019 In this study, a novel method based on the random forest is presented to solve the problem of track initiation in the air-traffic-control (ATC) radar system. ATC radar is the most common civilian surveillance radar. There are dense targets with different
Shuo Liu +4 more
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PeerJ, 2018 Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process.
T. Hengl +4 more
semanticscholar +1 more source
Visualizing Random Forest with Self-Organising Map
, 2014 Random Forest (RF) is a powerful ensemble method for classification and regression tasks. It consists of decision trees set. Although, a single tree is well interpretable for human, the ensemble of trees is a black-box model.
Płoński, Piotr, Zaremba, Krzysztof
core +1 more source
Data-driven multinomial random forest: a new random forest variant with strong consistency
Journal of Big DataIn this paper, we modify the proof methods of some previously weakly consistent variants of random forest into strongly consistent proof methods, and improve the data utilization of these variants in order to obtain better theoretical properties and ...
JunHao Chen, XueLi Wang, Fei Lei
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Journal of Combinatorial Theory, Series A, 1999 Suppose that rooted forests (in which the edges in each tree are directed away from the root of the tree) are formed by starting with a set of \(n\) labelled vertices and succesively adding an edge \(uv\) from a randomly chosen vertex \(u\) to the root \(v\) of a randomly chosen tree not containing \(u\). The author derives several enumeration formulae
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, 2020 In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the ...
E. Y. Boateng, Joseph Otoo., D. Abaye
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Frontiers in Earth Science, 2021 Landslides are widely distributed worldwide and often result in tremendous casualties and economic losses, especially in the Loess Plateau of China.
Shibao Wang +5 more
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