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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 1211–1220 of 2050 papers

TitleStatusHype
Gaussian Mixture Clustering Using Relative Tests of Fit—0
Gaussian Process-based Spatial Reconstruction of Electromagnetic fields—0
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria—0
General Bayesian time-varying parameter VARs for predicting government bond yields—0
General Hannan and Quinn Criterion for Common Time Series—0
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces—0
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression—0
Generalized Additive Model Selection—0
Generalized Information Criteria for Structured Sparse Models—0
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