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pro vyhledávání: '"Burke, Kevin"'
Autor:
McInerney, Andrew, Burke, Kevin
Feedforward neural networks (FNNs) are typically viewed as pure prediction algorithms, and their strong predictive performance has led to their use in many machine-learning applications. However, their flexibility comes with an interpretability trade
Externí odkaz:
http://arxiv.org/abs/2311.08139
Autor:
O'Neill, Meadhbh, Burke, Kevin
Datasets with extreme observations and/or heavy-tailed error distributions are commonly encountered and should be analyzed with careful consideration of these features from a statistical perspective. Small deviations from an assumed model, such as th
Externí odkaz:
http://arxiv.org/abs/2212.07317
Mean-field equations have been developed recently to approximate the dynamics of the Deffuant model of opinion formation. These equations can describe both fully-mixed populations and the case where individuals interact only along edges of a network.
Externí odkaz:
http://arxiv.org/abs/2210.07167
Autor:
McInerney, Andrew, Burke, Kevin
Feedforward neural networks (FNNs) can be viewed as non-linear regression models, where covariates enter the model through a combination of weighted summations and non-linear functions. Although these models have some similarities to the approaches u
Externí odkaz:
http://arxiv.org/abs/2207.04248
Publikováno v:
2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI) (2021) 659-664
Process visualizations of data from manufacturing execution systems (MESs) provide the ability to generate valuable insights for improved decision-making. Industry 4.0 is awakening a digital transformation where advanced analytics and visualizations
Externí odkaz:
http://arxiv.org/abs/2201.06465
We consider a parametric modelling approach for survival data where covariates are allowed to enter the model through multiple distributional parameters, i.e., scale and shape. This is in contrast with the standard convention of having a single covar
Externí odkaz:
http://arxiv.org/abs/2111.08573
Autor:
O'Neill, Meadhbh, Burke, Kevin
Modern variable selection procedures make use of penalization methods to execute simultaneous model selection and estimation. A popular method is the LASSO (least absolute shrinkage and selection operator), the use of which requires selecting the val
Externí odkaz:
http://arxiv.org/abs/2110.02643