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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 1381–1390 of 2050 papers

TitleStatusHype
Structural-constrained Methods for the Identification of Unobservable False Data Injection Attacks in Power Systems—0
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking—0
DEPARA: Deep Attribution Graph for Deep Knowledge TransferabilityCode1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
Modeling User Behaviors in Machine Operation Tasks for Adaptive Guidance—0
A new approach in model selection for ordinal target variables—0
Rethinking Parameter Counting in Deep Models: Effective Dimensionality RevisitedCode1
Model Selection in Contextual Stochastic Bandit Problems—0
Approximate Cross-validation: Guarantees for Model Assessment and SelectionCode0
Determination of Latent Dimensionality in International Trade Flow—0
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