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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 301–325 of 2050 papers

TitleStatusHype
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
Boldness-Recalibration for Binary Event Predictions—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
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning—0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
Aggregation of Affine Estimators—0
A coupled-mechanisms modelling framework for neurodegeneration—0
A Review of Change of Variable Formulas for Generative Modeling—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
A Regret-Variance Trade-Off in Online Learning—0
Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification—0
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—0
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees—0
A Bayesian Approach to Network Modularity—0
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?—0
Choice modelling in the age of machine learning - discussion paper—0
Bridging factor and sparse models—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Bridging Information Criteria and Parameter Shrinkage for Model Selection—0
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