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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 731–740 of 2050 papers

TitleStatusHype
Competing Models—0
Comparison of model selection techniques for seafloor scattering statistics—0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables—0
A Symmetry-based Framework for Model Selection of Coral Reef Population Growth Models—0
ALMERIA: Boosting pairwise molecular contrasts with scalable methods—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
The Mismeasure of Man and Models: Evaluating Allocational Harms in Large Language Models—0
Comparison of Bayesian predictive methods for model selection—0
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval—0
Comparison between Suitable Priors for Additive Bayesian Networks—0
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