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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 1981–1990 of 2050 papers

TitleStatusHype
Combined l_1 and greedy l_0 penalized least squares for linear model selection—0
Hierarchical Block Structures and High-resolution Model Selection in Large Networks—0
Speedy Model Selection (SMS) for Copula Models—0
Likelihood Adaptively Modified Penalties—0
Risk-consistency of cross-validation with lasso-type procedures—0
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees—0
The Cluster Graphical Lasso for improved estimation of Gaussian graphical models—0
Fuzzy Fibers: Uncertainty in dMRI Tractography—0
Bridging Information Criteria and Parameter Shrinkage for Model Selection—0
An Efficient Model Selection for Gaussian Mixture Model in a Bayesian Framework—0
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