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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 1851–1860 of 2050 papers

TitleStatusHype
Sparse model selection in the highly under-sampled regime—0
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks—0
Fast model selection by limiting SVM training times—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
On Column Selection in Approximate Kernel Canonical Correlation Analysis—0
Active Learning Algorithms for Graphical Model Selection—0
Deep Learning For Smile Recognition—0
Cox process representation and inference for stochastic reaction-diffusion processes—0
Cognito: Automated Feature Engineering for Supervised Learning—0
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models—0
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