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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 1031–1040 of 2050 papers

TitleStatusHype
Event Data Association via Robust Model Fitting for Event-based Object Tracking—0
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles—0
DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluationCode0
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain GeneralizationCode1
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized RecommendationsCode0
abess: A Fast Best Subset Selection Library in Python and RCode1
A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-box Design Problems—0
On Model Selection Consistency of Lasso for High-Dimensional Ising Models—0
Hydra: A System for Large Multi-Model Deep LearningCode1
A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification—0
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