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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 1291–1300 of 2050 papers

TitleStatusHype
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings—0
Topological model selection: a case-study in tumour-induced angiogenesis—0
TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment—0
To Translate or Not to Translate: A Systematic Investigation of Translation-Based Cross-Lingual Transfer to Low-Resource Languages—0
To tree or not to tree? Assessing the impact of smoothing the decision boundaries—0
Modeling User Behaviors in Machine Operation Tasks for Adaptive Guidance—0
Towards a more efficient representation of imputation operators in TPOT—0
Towards an Unsupervised Method for Model Selection in Few-Shot Learning—0
Towards Arbitrary-View Face Alignment by Recommendation Trees—0
Towards a Theoretical Framework of Out-of-Distribution Generalization—0
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