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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 2041–2050 of 2050 papers

TitleStatusHype
Data-driven calibration of linear estimators with minimal penalties—0
On the Reliability of Clustering Stability in the Large Sample Regime—0
Model selection and velocity estimation using novel priors for motion patterns—0
An Homotopy Algorithm for the Lasso with Online Observations—0
Bolasso: model consistent Lasso estimation through the bootstrapCode0
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation—0
Predictive Matrix-Variate t Models—0
Robust Regression with Twinned Gaussian Processes—0
On Sparsity and Overcompleteness in Image Models—0
A Bayesian Approach to Network Modularity—0
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