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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 591–600 of 2050 papers

TitleStatusHype
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Dirichlet process mixture of Gaussian process functional regressions and its variational EM algorithm—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Dirichlet Process Parsimonious Mixtures for clustering—0
Bayesian leave-one-out cross-validation for large data—0
Bayesian Learning with Wasserstein Barycenters—0
Bayesian Model Selection of Stochastic Block Models—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
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