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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 1271–1280 of 2050 papers

TitleStatusHype
The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization—0
The Quality of the Covariance Selection Through Detection Problem and AUC Bounds—0
Thermodynamic Bayesian Inference—0
The Sample Complexity of Parameter-Free Stochastic Convex Optimization—0
The smooth output assumption, and why deep networks are better than wide ones—0
Determining Principal Component Cardinality through the Principle of Minimum Description Length—0
The supremum principle selects simple, transferable models—0
The Time-Varying Multivariate Autoregressive Index Model—0
The topology of large Open Connectome networks for the human brain—0
The Value of Information in Human-AI Decision-making—0
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