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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 1091–1100 of 2050 papers

TitleStatusHype
A multi-stage machine learning model on diagnosis of esophageal manometry—0
Can We Characterize Tasks Without Labels or Features?Code1
Practical Transferability Estimation for Image Classification Tasks—0
Towards Transferable Adversarial Perturbations with Minimum Norm—0
QuaPy: A Python-Based Framework for QuantificationCode1
Taming Nonconvexity in Kernel Feature Selection -- Favorable Properties of the Laplace Kernel—0
Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation—0
Last Layer Marginal Likelihood for Invariance LearningCode0
Time Series Anomaly Detection with label-free Model Selection—0
Model Selection for Bayesian AutoencodersCode0
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