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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 1241–1250 of 2050 papers

TitleStatusHype
Estimation vs Metrics: is QE Useful for MT Model Selection?—0
Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks—0
On the Impact of Communities on Semi-supervised Classification Using Graph Neural NetworksCode0
A Bayesian Perspective on Training Speed and Model Selection—0
Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19—0
Probing Task-Oriented Dialogue Representation from Language Models—0
Learning from missing data with the Latent Block Model—0
Spike and slab variational Bayes for high dimensional logistic regression—0
Model Selection for Signal Processing: a Minimum Error Approach and a General Performance Analysis—0
Model selection in reconciling hierarchical time seriesCode0
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