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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 701–710 of 2050 papers

TitleStatusHype
Exploring linguistic feature and model combination for speech recognition based automatic AD detection—0
Black-box Selective Inference via Bootstrapping—0
Differentially Private Learning with Margin Guarantees—0
Evaluating Meta-Regression Techniques: A Simulation Study on Heterogeneity in Location and Time—0
Differentially Private Generalized Linear Models Revisited—0
Evaluating Representations with Readout Model Switching—0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR—0
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?—0
Bayesian Interpolation with Deep Linear Networks—0
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