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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 1751–1760 of 2050 papers

TitleStatusHype
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell TimeCode0
High-dimensional classification by sparse logistic regressionCode0
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation—0
Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical ModelsCode0
Awkt: A Physicochemical Parameter Estimation Tool for Capillary Zone Electrophoresis—0
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes—0
Saliency Revisited: Analysis of Mouse Movements versus Fixations—0
Model Selection in Bayesian Neural Networks via Horseshoe PriorsCode0
Bayesian stochastic blockmodeling—0
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