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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 1541–1550 of 2050 papers

TitleStatusHype
Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression—0
Information criteria for non-normalized models—0
Automatic Model Selection for Neural Networks—0
Decision Making with Machine Learning and ROC Curves—0
Disentangling Factors of Variation Using Few Labels—0
Post-Selection Inference in Three-Dimensional Panel Data—0
S^2-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning—0
On Learning to Prove—0
Bayesian leave-one-out cross-validation for large data—0
A deep learning based solution for construction equipment detection: from development to deployment—0
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