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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 791–800 of 2050 papers

TitleStatusHype
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Find the dimension that counts: Fast dimension estimation and Krylov PCA—0
Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
Generalized Information Criteria for Structured Sparse Models—0
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
Greedy equivalence search for nonparametric graphical models—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
Bayesian Evidence and Model Selection—0
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling—0
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