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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 931–940 of 2050 papers

TitleStatusHype
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Improvement of Identification Procedure Using Hybrid Cuckoo Search Algorithm for TurbineGovernor and Excitation System—0
Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes—0
Improving Group Lasso for high-dimensional categorical data—0
Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning—0
Improving Model Robustness Using Causal Knowledge—0
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
Consistencies and inconsistencies between model selection and link prediction in networks—0
Consistent model selection in the spiked Wigner model via AIC-type criteria—0
Designing Ecosystems of Intelligence from First Principles—0
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