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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 1861–1870 of 2050 papers

TitleStatusHype
Efficient Sequential Decision Making with Large Language Models—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields—0
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis—0
Empirical analysis in limit order book modeling for Nikkei 225 Stocks with Cox-type intensities—0
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
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
Encoding-dependent generalization bounds for parametrized quantum circuits—0
End-to-End Edge AI Service Provisioning Framework in 6G ORAN—0
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