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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 771–780 of 2050 papers

TitleStatusHype
Active Learning Algorithms for Graphical Model Selection—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking—0
A Statistical-Modelling Approach to Feedforward Neural Network Model Selection—0
Clustering - What Both Theoreticians and Practitioners are Doing Wrong—0
A Statistical Framework for Model Selection in LSTM Networks—0
A Junction Tree Framework for Undirected Graphical Model Selection—0
Clustering evolving data using kernel-based methods—0
Clustering Discrete-Valued Time Series—0
Clustering-Based Validation Splits for Model Selection under Domain Shift—0
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