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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 781–790 of 2050 papers

TitleStatusHype
Improving classification performance by feature space transformations and model selection—0
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT—0
Federated Model Search via Reinforcement Learning—0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
Feedback-Controlled Sequential Lasso Screening—0
Causal Falling Rule Lists—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data—0
Generalized Information Criteria for Structured Sparse Models—0
Bayesian Evidence and Model Selection—0
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