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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 1041–1050 of 2050 papers

TitleStatusHype
Principled model selection for stochastic dynamics—0
Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets—0
Private Selection with Heterogeneous Sensitivities—0
Probabilistic latent variable models for distinguishing between cause and effect—0
Probabilistic models of individual and collective animal behavior—0
Probability Distribution on Full Rooted Trees—0
Probing Task-Oriented Dialogue Representation from Language Models—0
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits—0
Problem-dependent attention and effort in neural networks with applications to image resolution and model selection—0
Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization—0
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