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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 621–630 of 2050 papers

TitleStatusHype
Differentiable Model Selection for Ensemble LearningCode0
Driver Identification by an Ensemble of CNNs Obtained from Majority-Voting Model SelectionCode0
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
Learning Lie Group Symmetry Transformations with Neural NetworksCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Effects of sampling skewness of the importance-weighted risk estimator on model selectionCode0
Hybrid safe-strong rules for efficient optimization in lasso-type problemsCode0
Learning the mechanisms of network growthCode0
LEATHER: A Framework for Learning to Generate Human-like Text in DialogueCode0
Automatic Gradient BoostingCode0
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