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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 271–280 of 2050 papers

TitleStatusHype
A Survey on Theoretical Advances of Community Detection in Networks—0
eGAD! double descent is explained by Generalized Aliasing Decomposition—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks—0
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance—0
Active Learning for Undirected Graphical Model Selection—0
A study on the distribution of social biases in self-supervised learning visual models—0
A Latent Gaussian Mixture Model for Clustering Longitudinal Data—0
A Bayesian Perspective on Training Speed and Model Selection—0
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