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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 811–820 of 2050 papers

TitleStatusHype
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
Fixed effects testing in high-dimensional linear mixed models—0
Fuzzy Fibers: Uncertainty in dMRI Tractography—0
Choice modelling in the age of machine learning - discussion paper—0
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles—0
A Rule-Based Epidemiological Modelling Framework—0
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