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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 1431–1440 of 2050 papers

TitleStatusHype
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth—0
Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial—0
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models—0
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees—0
A coupled-mechanisms modelling framework for neurodegeneration—0
A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI—0
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