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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 991–1000 of 2050 papers

TitleStatusHype
Is it worth it? Budget-related evaluation metrics for model selection—0
Is K-fold cross validation the best model selection method for Machine Learning?—0
Factor-Augmented Regularized Model for Hazard Regression—0
Face Recognition using Optimal Representation Ensemble—0
IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation—0
Joint Continuous and Discrete Model Selection via Submodularity—0
Face Recognition Using Deep Multi-Pose Representations—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
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