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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 721–730 of 2050 papers

TitleStatusHype
Exact Dimensionality Selection for Bayesian PCA—0
Exact Post Model Selection Inference for Marginal Screening—0
Bayesian high-dimensional linear regression with generic spike-and-slab priors—0
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models—0
Breaking the bonds of weak coupling: the dynamic causal modelling of oscillator amplitudes—0
Experiment Planning with Function Approximation—0
Expert Finding in Community Question Answering: A Review—0
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries—0
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations—0
DGSAC: Density Guided Sampling and Consensus—0
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