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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 1341–1350 of 2050 papers

TitleStatusHype
Quantized Neural Networks: Characterization and Holistic Optimization—0
Sig-SDEs model for quantitative finance—0
Solution Path Algorithm for Twin Multi-class Support Vector MachineCode0
Revealing consensus and dissensus between network partitions—0
Selective Inference for Latent Block Models—0
Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model—0
Learning Equations from Biological Data with Limited Time SamplesCode0
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law—0
Marginal likelihood computation for model selection and hypothesis testing: an extensive review—0
Forward utilities and Mean-field games under relative performance concerns—0
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