Results 1 to 10 of about 5,516,106 (266)
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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Unsupervised random forests. [PDF]
Stat Anal Data Min, 2021 sidClustering is a new random forests unsupervised machine learning algorithm. The first step in sidClustering involves what is called sidification of the features: staggering the features to have mutually exclusive ranges (called the staggered ...
Mantero A, Ishwaran H.
europepmc +5 more sources
Machine Learning, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
L. Breiman
semanticscholar +4 more sources
Generalized random forests [PDF]
The Annals of Statistics, 2016 We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations.
S. Athey, J. Tibshirani, Stefan Wager
semanticscholar +6 more sources
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
doaj +2 more sources
A comparison among interpretative proposals for Random Forests
Machine Learning with Applications, 2021 The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields.
Massimo Aria +2 more
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Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency [PDF]
IEEE Transactions on Cybernetics, 2020 The original random forests (RFs) algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of RFs lags far behind its applications.
Jianyuan Sun +5 more
semanticscholar +5 more sources
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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International Journal of Approximate Reasoning, 2010 AbstractWhen individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision ...
Piero P. Bonissone +3 more
openalex +3 more sources