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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 1941–1950 of 2050 papers

TitleStatusHype
Unsupervised learning of regression mixture models with unknown number of components—0
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection—0
"Look Ma, No Hands!" A Parameter-Free Topic Model—0
Kernel-based Information Criterion—0
Robust Graphical Modeling with t-Distributions—0
Volumes of logistic regression models with applications to model selection—0
Sparse Partially Linear Additive ModelsCode0
Nonparametric Hierarchical Clustering of Functional Data—0
Techniques for clustering interaction data as a collection of graphs—0
Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs—0
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