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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 1141–1150 of 2050 papers

TitleStatusHype
Evaluating the Utility of Model Explanations for Model Development—0
Evaluating Word Embeddings on Low-Resource Languages—0
Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage—0
e-Values for Real-Time Residential Electricity Demand Forecast Model Selection—0
Evasion Attacks against Machine Learning at Test Time—0
Event Data Association via Robust Model Fitting for Event-based Object Tracking—0
Exact Dimensionality Selection for Bayesian PCA—0
Exact Post Model Selection Inference for Marginal Screening—0
Exact post-selection inference, with application to the lasso—0
Experiment Planning with Function Approximation—0
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