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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 1451–1460 of 2050 papers

TitleStatusHype
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems—0
Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control—0
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles—0
Model selection for deep audio source separation via clustering analysis—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations—0
Gaussian Mixture Clustering Using Relative Tests of Fit—0
Effective Stabilized Self-Training on Few-Labeled Graph DataCode0
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