Results 131 to 140 of about 6,917,983 (390)
Topological Properties of International Commodity Market: How Uncertainty Affects the Linkages?
Agribusiness, EarlyView.ABSTRACT The study aims to explore the network topology of the international commodity market by examining the interconnections among 21 commodity futures across various categories, including energy, precious and industrial metals, and agriculture. We analyze the market structure of these commodity futures under both low and high uncertainty conditions
Ibrahim Yagli, Bayram Deviren
wiley +1 more source
A review of word-sense disambiguation methods and algorithms: Introduction
Transactions of the Karelian Research Centre of the Russian Academy of Sciences, 2015 The word-sense disambiguation task is a classification task, where the goal is to predict the meaning of words and phrases with the help of surrounding text.
Tatiana Kaushinis +14 more
doaj +1 more source
Bayesian topology identification of linear dynamic networks [PDF]
arXiv, 2019 In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper, we explore a Bayesian model selection method for identifying the connectivity of networks of transfer functions, without the need to estimate the dynamics.
arxiv
A Bayesian method for the induction of probabilistic networks from data [PDF]
, 1992 Gregory F. Cooper, Edward H. Herskovits
openalex +1 more source
Advancements in Machine Learning for Microrobotics in Biomedicine
Advanced Intelligent Systems, EarlyView.Microrobotics is an innovative technology with great potential for noninvasive medical interventions. However, controlling and imaging microrobots pose significant challenges in complex environments and in living organisms. This review explores how machine learning algorithms can address these issues, offering solutions for adaptive motion control and ...
Amar Salehi +6 more
wiley +1 more source
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data [PDF]
, 1994 David Heckerman +2 more
openalex +1 more source
Graphs for Margins of Bayesian Networks [PDF]
Scandinavian Journal of Statistics, 2015 AbstractDirected acyclic graph (DAG) models—also called Bayesian networks—are widely used in probabilistic reasoning, machine learning and causal inference. If latent variables are present, then the set of possible marginal distributions over the remaining (observed) variables is generally not represented by any DAG.
openaire +3 more sources
Advanced Intelligent Systems, EarlyView.A portable, wearable device based on metabolic heat integrated sensing and deep learning enables continuous blood glucose (BG) monitoring. The system uses a gate recurrent unit model for real‐time BG prediction, achieving accuracy comparable to commercial noninvasive meters.
Haolin Wang +12 more
wiley +1 more source
Bayesian Learning of Neural Networks for Signal/Background Discrimination in Particle Physics [PDF]
arXiv, 2007 Neural networks are used extensively in classification problems in particle physics research. Since the training of neural networks can be viewed as a problem of inference, Bayesian learning of neural networks can provide more optimal and robust results than conventional learning methods.
arxiv
Backward Simulation in Bayesian Networks [PDF]
, 1994 Robert Fung, Brendan Del Favero
openalex +1 more source