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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 901–910 of 2050 papers

TitleStatusHype
Bayesian Boosting for Linear Mixed Models—0
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness—0
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets—0
How to select predictive models for causal inference?—0
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective—0
Improving Model Robustness Using Causal Knowledge—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection—0
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model—0
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