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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 951–960 of 2050 papers

TitleStatusHype
Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity—0
Continuous Bayesian Model Selection for Multivariate Causal Discovery—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Sensitivity to control signals in triphasic rhythmic neural systems: a comparative mechanistic analysis via infinitesimal local timing response curves—0
Inferring bias and uncertainty in camera calibration—0
Convergence Properties of Kronecker Graphical Lasso Algorithms—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
Inferring Latent dimension of Linear Dynamical System with Minimum Description Length—0
Inferring Network Structure From Data—0
Bayesian Anomaly Detection and Classification—0
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