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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 581–590 of 2050 papers

TitleStatusHype
4-D Epanechnikov Mixture Regression in Light Field Image Compression—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Dimensionality Detection and Integration of Multiple Data Sources via the GP-LVM—0
Dimensionality Dependent PAC-Bayes Margin Bound—0
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors—0
An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
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