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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 2011–2020 of 2050 papers

TitleStatusHype
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
Online Learning with Predictable Sequences—0
Model Selection for Degree-corrected Block Models—0
Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection—0
Fast Cross-Validation via Sequential TestingCode0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables—0
Convergence Properties of Kronecker Graphical Lasso Algorithms—0
A Multi-objective Exploratory Procedure for Regression Model Selection—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
PAC-Bayesian Policy Evaluation for Reinforcement Learning—0
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