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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 1341–1350 of 2050 papers

TitleStatusHype
Large-Scale Model Selection with Misspecification—0
lassopack: Model selection and prediction with regularized regression in Stata—0
Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs—0
Latent Time Neural Ordinary Differential Equations—0
Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP)—0
LEAD: Exploring Logit Space Evolution for Model Selection—0
Learning Adaptive Value of Information for Structured Prediction—0
Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks—0
Learning conditional independence structure for high-dimensional uncorrelated vector processes—0
Learning Curves for Decision Making in Supervised Machine Learning: A Survey—0
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