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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 1821–1830 of 2050 papers

TitleStatusHype
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Dirichlet process mixture of Gaussian process functional regressions and its variational EM algorithm—0
Dirichlet Process Parsimonious Mixtures for clustering—0
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound—0
Discriminative Clustering by Regularized Information Maximization—0
Disentangling Factors of Variations Using Few Labels—0
Disentangling Factors of Variation Using Few Labels—0
Distributed Bayesian Piecewise Sparse Linear Models—0
Distributed filtered hyperinterpolation for noisy data on the sphere—0
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