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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 301–310 of 2050 papers

TitleStatusHype
A simple application of FIC to model selection—0
Unsupervised Model Selection for Variational Disentangled Representation Learning—0
A Sentiment Analysis of Medical Text Based on Deep Learning—0
A Rule-Based Epidemiological Modelling Framework—0
A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI—0
A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: application to porcine aortic valves—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor—0
Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities—0
Agreement-based Learning—0
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