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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 1201–1210 of 2050 papers

TitleStatusHype
Sparse Inverse Covariance Estimation with Calibration—0
Sparse Modeling for Image and Vision Processing—0
Sparse model selection in the highly under-sampled regime—0
Sparse model selection via integral terms—0
Sparse Models for Machine Learning—0
Sparse Private LASSO Logistic Regression—0
Predictor Selection for Synthetic Controls—0
Robust Model Selection and Nearly-Proper Learning for GMMs—0
Sparsified Simultaneous Confidence Intervals for High-Dimensional Linear Models—0
Sparsistent Learning of Varying-coefficient Models with Structural Changes—0
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