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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 1391–1400 of 2050 papers

TitleStatusHype
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs—0
Learning Gaussian Graphical Models via Multiplicative Weights—0
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation—0
Impact of ImageNet Model Selection on Domain AdaptationCode0
Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection—0
Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case—0
Learning the Hypotheses Space from data Part II: Convergence and Feasibility—0
Blocked Clusterwise Regression—0
LIBTwinSVM: A Library for Twin Support Vector MachinesCode1
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
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