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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 651–660 of 2050 papers

TitleStatusHype
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
Efficient Sequential Decision Making with Large Language Models—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields—0
Adaptive Sequential Machine Learning—0
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis—0
Empirical analysis in limit order book modeling for Nikkei 225 Stocks with Cox-type intensities—0
Achieving Fairness with a Simple Ridge Penalty—0
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
Estimating Stable Fixed Points and Langevin Potentials for Financial Dynamics—0
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