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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 1091–1100 of 2050 papers

TitleStatusHype
Utilizing AI Language Models to Identify Prognostic Factors for Coronary Artery Disease: A Study in Mashhad Residents—0
U-Trustworthy Models.Reliability, Competence, and Confidence in Decision-Making—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Validate on Sim, Detect on Real -- Model Selection for Domain Randomization—0
Validating the predictions of mathematical models describing tumor growth and treatment response—0
Variance function estimation in regression model via aggregation procedures—0
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction—0
Variational approach for learning Markov processes from time series data—0
Variational Bayes for high-dimensional linear regression with sparse priors—0
Variational Inference and Learning of Piecewise-linear Dynamical Systems—0
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