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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 1521–1530 of 2050 papers

TitleStatusHype
Distributionally Robust Formulation and Model Selection for the Graphical LassoCode0
Adaptive Model Selection Framework: An Application to Airline Pricing—0
Catastrophic forgetting: still a problem for DNNsCode0
Analysis of the AutoML Challenge Series 2015–2018—0
Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood MaximizationCode0
Gmail Smart Compose: Real-Time Assisted Writing—0
Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression—0
Automatic Model Selection for Neural Networks—0
Information criteria for non-normalized models—0
Decision Making with Machine Learning and ROC Curves—0
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