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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 2001–2010 of 2050 papers

TitleStatusHype
Asymptotic Model Selection for Directed Networks with Hidden Variables—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
Minimally-Supervised Morphological Segmentation using Adaptor Grammars—0
Mixture Model Averaging for Clustering—0
Active Comparison of Prediction Models—0
Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs—0
Dimensionality Dependent PAC-Bayes Margin Bound—0
Weighted Likelihood Policy Search with Model Selection—0
Deep Gaussian Processes—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
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