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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 1491–1500 of 2050 papers

TitleStatusHype
Model Selection Through Model Sorting—0
Model Selection via MCRB Optimization—0
Model selection with Gini indices under auto-calibration—0
Model Selection With Graphical Neighbour Information—0
Model Selection for Generic Reinforcement Learning—0
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation—0
Model-specific Data Subsampling with Influence Functions—0
Model Validation and Selection in Metabolic Flux Analysis and Flux Balance Analysis—0
MODfinity: Unsupervised Domain Adaptation with Multimodal Information Flow Intertwining—0
MOFA: Modular Factorial Design for Hyperparameter Optimization—0
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