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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 1461–1470 of 2050 papers

TitleStatusHype
Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso—0
Model Selection for Anomaly Detection—0
Model Selection for Average Reward RL with Application to Utility Maximization in Repeated Games—0
Model selection for behavioral learning data and applications to contextual bandits—0
Model Selection for Cross-Lingual Transfer using a Learned Scoring Function—0
Model selection for deep audio source separation via clustering analysis—0
Model Selection for Degree-corrected Block Models—0
Model Selection for Gaussian-gated Gaussian Mixture of Experts Using Dendrograms of Mixing Measures—0
Model Selection for Gaussian Process Regression by Approximation Set Coding—0
Model Selection for Generalized Zero-shot Learning—0
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