Results 1 to 10 of about 66,954 (113)
Covariance regression with random forests [PDF]
BMC Bioinformatics, 2023 Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine.
Cansu Alakus +2 more
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Random forests, sound symbolism and Pokémon evolution. [PDF]
PLoS ONE, 2023 This study constructs machine learning algorithms that are trained to classify samples using sound symbolism, and then it reports on an experiment designed to measure their understanding against human participants.
Alexander James Kilpatrick +2 more
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Functional random forests for curve response [PDF]
Scientific Reports, 2021 The rapid advancement of functional data in various application fields has increased the demand for advanced statistical approaches that can incorporate complex structures and nonlinear associations.
Guifang Fu, Xiaotian Dai, Yeheng Liang
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Phylogeny-informed random forests for human microbiome studies [PDF]
Microbiology SpectrumRandom Forest is a widely used tree-based ensemble learning algorithm that efficiently captures complex nonlinear relationships and higher-order feature interactions with no distributional assumptions to be satisfied.
Hyunwook Koh
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Posture-invariant myoelectric control with self-calibrating random forests [PDF]
Frontiers in NeuroroboticsIntroductionMyoelectric control systems translate different patterns of electromyographic (EMG) signals into the control commands of diverse human-machine interfaces via hand gesture recognition, enabling intuitive control of prosthesis and immersive ...
Xinyu Jiang +2 more
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On random trees and forests [PDF]
ESAIM: Proceedings and Surveys, 2023 The first talk at the session Random trees and random forests “Journée MAS” (27/08/2021) was presented by I. Kortchemski. After a general up-to-date introduction to local and scaling limits of Bienaymé trees (which are discrete branching trees), he ...
Contat Alice +4 more
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IEEE Access, 2022 Random forests of axis-parallel decision trees still show competitive accuracy in various tasks; however, they have drawbacks that limit their applicability. Namely, they perform poorly for multidimensional sparse data.
Dmitry A. Devyatkin, Oleg G. Grigoriev
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Random Forests for Time Series
Revstat Statistical Journal, 2023 Random forests are a powerful learning algorithm. However, when dealing with time series, the time-dependent structure is lost, assuming the observations are independent. We propose some variants of random forests for time series. The idea is to replace
Benjamin Goehry +4 more
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Tracking Cloud Forests With Cloud Technology and Random Forests
Frontiers in Environmental Science, 2021 Hotspots of endemic biodiversity, tropical cloud forests teem with ecosystem services such as drinking water, food, building materials, and carbon sequestration. Unfortunately, already threatened by climate change, the cloud forests in our study area are
Pasky Pascual, Cam Pascual
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Multi-Class Assessment Based on Random Forests
Education Sciences, 2021 Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their
Mehdi Berriri +3 more
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