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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 1001–1010 of 2050 papers

TitleStatusHype
Face Recognition using Optimal Representation Ensemble—0
Kernel-based Information Criterion—0
Face Recognition Using Deep Multi-Pose Representations—0
KITE: A Kernel-based Improved Transferability Estimation Method—0
Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering—0
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects—0
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition—0
Boldness-Recalibration for Binary Event Predictions—0
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