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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 1251–1260 of 2050 papers

TitleStatusHype
Cost-based feature selection for network model choice—0
Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
General Hannan and Quinn Criterion for Common Time Series—0
Detecting seasonal episodic-like spatiotemporal memory patterns using animal movement modelling—0
Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way—0
Rethinking Parameter Counting: Effective Dimensionality Revisited—0
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?—0
Continual Learning Without Knowing Task Identities: Rethinking Occam's Razor—0
Explainable Multi-class Classification of Medical Data—0
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