Results 81 to 90 of about 103,187 (313)
Oblique Bayesian additive regression trees
Trans. Mach. Learn. Res.Current implementations of Bayesian Additive Regression Trees (BART) are based on axis-aligned decision rules that recursively partition the feature space using a single feature at a time. Several authors have demonstrated that oblique trees, whose decision rules are based on linear combinations of features, can sometimes yield better predictions than ...
Paul-Hieu V. Nguyen +2 more
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Bayesian Detection in Bounded Height Tree Networks [PDF]
IEEE Transactions on Signal Processing, 2007 We study the asymptotic detection performance of large sensor networks, configured as trees with bounded height, in which information is progressively compressed as it moves towards the root of the tree. We show that the error probability decays exponentially fast, and we provide bounds for the error exponent. We analyze further the case where the tree
Wee-Peng Tay +2 more
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Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Mechanics & IndustryIn recent years, the number of wind farms and the power of wind turbines have been greatly improved, and the gearing system, as a key structure in doubly-fed wind turbines, is of great significance to the safe and stable operation of wind turbines ...
Yin Xiaowei +3 more
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Fidelity of hyperbolic space for Bayesian phylogenetic inference.
PLoS Computational Biology, 2023 Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. However, Bayesian phylogenetics faces the challenging computational problem of moving throughout the high-dimensional space of trees.
Matthew Macaulay +2 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
RJHMC-Tree for Exploration of the Bayesian Decision Tree Posterior
Bayesian AnalysisDecision trees have found widespread application within the machine learning community due to their flexibility and interpretability. This paper is directed towards learning decision trees from data using a Bayesian approach, which is challenging due to the potentially enormous parameter space required to span all tree models.
Jodie A. Cochrane +2 more
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Bayesian Learning of Clique Tree Structure
CoRR, 2017 The problem of categorical data analysis in high dimensions is considered. A discussion of the fundamental difficulties of probability modeling is provided, and a solution to the derivation of high dimensional probability distributions based on Bayesian learning of clique tree decomposition is presented.
Cetin Savkli +3 more
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