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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 1551–1560 of 2050 papers

TitleStatusHype
ODIN: Automated Drift Detection and Recovery in Video Analytics—0
Off-Policy Evaluation via Off-Policy Classification—0
On an improvement of LASSO by scaling—0
On Bayesian Exponentially Embedded Family for Model Order Selection—0
On Column Selection in Approximate Kernel Canonical Correlation Analysis—0
On Creating a Causally Grounded Usable Rating Method for Assessing the Robustness of Foundation Models Supporting Time Series—0
On discretely structured growth models and their moments—0
One Step Is Enough for Few-Shot Cross-Lingual Transfer: Co-Training with Gradient Optimization—0
A review of Gaussian Markov models for conditional independence—0
On hyperparameter tuning in general clustering problemsm—0
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