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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 831–840 of 2050 papers

TitleStatusHype
Determination of Latent Dimensionality in International Trade Flow—0
Generalized Information Criteria for Structured Sparse Models—0
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables—0
Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory—0
A Bayesian Model for Bivariate Causal Inference—0
Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems—0
Generative diffusion model surrogates for mechanistic agent-based biological models—0
Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier—0
A Statistical Framework for Model Selection in LSTM Networks—0
Accessible, At-Home Detection of Parkinson's Disease via Multi-task Video Analysis—0
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