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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 1201–1210 of 2050 papers

TitleStatusHype
Forward utilities and Mean-field games under relative performance concerns—0
Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models—0
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection—0
From Human Annotation to LLMs: SILICON Annotation Workflow for Management Research—0
From Structured to Unstructured:A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs—0
Functional additive models on manifolds of planar shapes and forms—0
Fundamental limits to learning closed-form mathematical models from data—0
Fusion Subspace Clustering for Incomplete Data—0
Fuzzy Fibers: Uncertainty in dMRI Tractography—0
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles—0
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