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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 1651–1660 of 2050 papers

TitleStatusHype
Learning Equations for Extrapolation and ControlCode0
Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical ModelCode0
Structured Variational Learning of Bayesian Neural Networks with Horseshoe PriorsCode0
Stationary Geometric Graphical Model Selection—0
Degrees of Freedom and Model Selection for k-means ClusteringCode0
Agreement-based Learning—0
Structural Learning of Multivariate Regression Chain Graphs via Decomposition—0
An Approach to the CLPsych 2018 Shared Task Using Top-Down Text Representation and Simple Bottom-Up Model Selection—0
Bayesian Learning with Wasserstein Barycenters—0
A Local Information Criterion for Dynamical Systems—0
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