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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 1571–1580 of 2050 papers

TitleStatusHype
Online Model Selection for Reinforcement Learning with Function Approximation—0
On model misspecification and KL separation for Gaussian graphical models—0
On Model Selection Consistency of Lasso for High-Dimensional Ising Models—0
On model selection consistency of penalized M-estimators: a geometric theory—0
On model selection consistency of regularized M-estimators—0
On Quantum Ambiguity and Potential Exponential Computational Speed-Ups to Solving Dynamic Asset Pricing Models—0
On Sparsity and Overcompleteness in Image Models—0
On summarized validation curves and generalization—0
On the calibration of underrepresented classes in LiDAR-based semantic segmentation—0
On the complexity of logistic regression models—0
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