Results 41 to 50 of about 5,516,106 (266)
Tuning parameters in random forests
ESAIM: Proceedings and Surveys, 2017 Breiman's (2001) random forests are a very popular class of learning algorithms often able to produce good predictions even in high-dimensional frameworks, with no need to accurately tune its inner parameters.
Scornet Erwan
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Segmentation of PMSE Data Using Random Forests
Remote Sensing, 2022 EISCAT VHF radar data are used for observing, monitoring, and understanding Earth’s upper atmosphere. This paper presents an approach to segment Polar Mesospheric Summer Echoes (PMSE) from datasets obtained from EISCAT VHF radar data. The data consist of
Dorota Jozwicki +3 more
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Sankhya A, 2018 Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid procedures that we call neural ...
Biau, Gérard +2 more
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Remote Sensing, 2020 The optimization of crop nitrogen fertilization to accurately predict and match the nitrogen (N) supply to the crop N demand is the subject of intense research due to the environmental and economic impact of N fertilization.
Hwang Lee, Jinfei Wang, B. Leblon
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Coalescent Random Forests [PDF]
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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Soil Mapping Based on the Integration of the Similarity-Based Approach and Random Forests
Land, 2020 Digital soil mapping (DSM) is currently the primary framework for predicting the spatial variation of soil information (soil type or soil properties). Random forests and similarity-based methods have been used widely in DSM.
Desheng Wang, A-Xing Zhu
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Classification of GLM Flashes Using Random Forests
Earth and Space Science, 2021 [The Geostationary Lightning Mapper (GLM) detects total lightning continuously from space, and does not distinguish intra‐cloud (IC) from cloud‐to‐ground (CG) lightning. This research focuses on differentiating CG and IC lightning detected by GLM using a
Jacquelyn Ringhausen +3 more
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Continuous Dynamic Update of Fuzzy Random Forests
International Journal of Computational Intelligence Systems, 2022 Fuzzy random forests are well-known machine learning classification mechanisms based on a collection of fuzzy decision trees. An advantage of using fuzzy rules is the possibility to manage uncertainty and to work with linguistic scales.
Jordi Pascual-Fontanilles +3 more
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Applied Sciences, 2021 Omnidirectional honeybee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a beehive over a period of time.
Vladimir Kulyukin +2 more
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Journal of Thoracic Oncology, 2011 In the article by Chen et al,1 the authors used Random Survival Forests (RSF) as part of their approach for analyzing the data. In this note, we will explain RSF in a nontechnical way; precise details of the RSF method are described in the article by Ishwaran et al.2 RSF are an adaptation of Random Forests (RF)3 designed to be used for survival data ...
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