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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 1951–1960 of 2050 papers

TitleStatusHype
Reliable ABC model choice via random forests—0
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning—0
SCAMS: Simultaneous Clustering and Model Selection—0
Learning with many experts: model selection and sparsity—0
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models—0
Model Consistency of Partly Smooth Regularizers—0
Learning from Domain Complexity—0
Active Learning for Undirected Graphical Model Selection—0
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection—0
Selective Factor Extraction in High Dimensions—0
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