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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 1171–1180 of 2050 papers

TitleStatusHype
Factorized Asymptotic Bayesian Inference for Latent Feature Models—0
Factors in Fashion: Factor Analysis towards the Mode—0
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models—0
Fast and fully-automated histograms for large-scale data sets—0
Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities—0
Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)—0
Fast Linear Model Trees by PILOT—0
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