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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 771–780 of 2050 papers

TitleStatusHype
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria—0
Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)—0
Fast Linear Model Trees by PILOT—0
Fast model selection by limiting SVM training times—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
Fast rates with high probability in exp-concave statistical learning—0
Fast sampling and model selection for Bayesian mixture models—0
Cats & Co: Categorical Time Series Coclustering—0
Feature-based model selection for object detection from point cloud data—0
Geometric and Topological Inference for Deep Representations of Complex Networks—0
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