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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 461–470 of 2050 papers

TitleStatusHype
Confidence-based Ensembles of End-to-End Speech Recognition Models—0
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Conformal Prediction with Upper and Lower Bound Models—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Conjugate Mixture Models for Clustering Multimodal Data—0
Consensual Aggregation on Random Projected High-dimensional Features for Regression—0
Consistencies and inconsistencies between model selection and link prediction in networks—0
Consistent model selection in the spiked Wigner model via AIC-type criteria—0
Data-driven model selection within the matrix completion method for causal panel data models—0
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