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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 1361–1370 of 2050 papers

TitleStatusHype
Using anomaly detection to support classification of fast running (packaging) processes—0
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies—0
Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications—0
Using Kernel Methods and Model Selection for Prediction of Preterm Birth—0
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
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