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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 661–670 of 2050 papers

TitleStatusHype
Empirical Quantitative Analysis of COVID-19 Forecasting Models—0
Empowering Agricultural Insights: RiceLeafBD - A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique—0
Achieving Fairness with a Simple Ridge Penalty—0
Encoding-dependent generalization bounds for parametrized quantum circuits—0
Evaluating Gender Bias in Large Language Models—0
An Optimal Likelihood Free Method for Biological Model Selection—0
Energy-Aware Dynamic Neural Inference—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum—0
Differentially Private Learning with Margin Guarantees—0
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