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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 1141–1150 of 2050 papers

TitleStatusHype
4-D Epanechnikov Mixture Regression in Light Field Image Compression—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Inferring bias and uncertainty in camera calibration—0
Order Book Queue Hawkes-Markovian Modeling—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Model-Parallel Model Selection for Deep Learning Systems—0
Model Selection for Generic Reinforcement Learning—0
Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities—0
Gaussian Process Subspace Regression for Model ReductionCode0
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential EquationsCode0
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