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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 1871–1880 of 2050 papers

TitleStatusHype
A Test of Relative Similarity For Model Selection in Generative ModelsCode0
Block-diagonal covariance selection for high-dimensional Gaussian graphical models—0
NYTRO: When Subsampling Meets Early StoppingCode0
Causal Falling Rule Lists—0
GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnaceCode0
Higher-order asymptotics for the parametric complexity—0
A Statistical Theory of Deep Learning via Proximal Splitting—0
Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics—0
Deep Online Convex Optimization by Putting Forecaster to Sleep—0
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis—0
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