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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 1531–1540 of 2050 papers

TitleStatusHype
Disentangling Factors of Variation Using Few Labels—0
Interpretable multiclass classification by MDL-based rule listsCode1
Post-Selection Inference in Three-Dimensional Panel Data—0
On Learning to Prove—0
S^2-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning—0
Bayesian leave-one-out cross-validation for large data—0
BERTScore: Evaluating Text Generation with BERTCode1
A deep learning based solution for construction equipment detection: from development to deployment—0
Forecasting with time series imagingCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
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