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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 1181–1190 of 2050 papers

TitleStatusHype
Graph Similarity Description: How Are These Graphs Similar?—0
Model Selection for Production System via Automated Online Experiments—0
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile—0
Vector autoregression models with skewness and heavy tails—0
Informative Bayesian model selection for RR Lyrae star classifiers—0
Hypothesis Testing for Equality of Latent Positions in Random Graphs—0
Laplace Redux - Effortless Bayesian Deep Learning—0
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy—0
On-the-fly learning of adaptive strategies with bandit algorithms—0
Towards Model Selection using Learning Curve Cross-ValidationCode0
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