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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 171–180 of 2050 papers

TitleStatusHype
Utilizing AI Language Models to Identify Prognostic Factors for Coronary Artery Disease: A Study in Mashhad Residents—0
Empowering Agricultural Insights: RiceLeafBD - A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique—0
On the use of Statistical Learning Theory for model selection in Structural Health Monitoring—0
Fast sampling and model selection for Bayesian mixture models—0
An Investigation into Seasonal Variations in Energy Forecasting for Student Residences—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
Unsupervised detection and fitness estimation of emerging SARS-CoV-2 variants. Application to wastewater samples (ANRS0160)Code0
Private Selection with Heterogeneous Sensitivities—0
An Instrumental Variables Approach to Testing Firm Conduct—0
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