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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 961–970 of 2050 papers

TitleStatusHype
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
Bayesian Boosting for Linear Mixed Models—0
Is it worth it? Budget-related evaluation metrics for model selection—0
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model—0
Information criteria for non-normalized models—0
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization—0
Informative Bayesian model selection for RR Lyrae star classifiers—0
Correcting Model Bias with Sparse Implicit Processes—0
Bayesian Anomaly Detection and Classification—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
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