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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 1021–1030 of 2050 papers

TitleStatusHype
To tree or not to tree? Assessing the impact of smoothing the decision boundaries—0
Modeling User Behaviors in Machine Operation Tasks for Adaptive Guidance—0
Towards a more efficient representation of imputation operators in TPOT—0
Towards an Unsupervised Method for Model Selection in Few-Shot Learning—0
Towards Arbitrary-View Face Alignment by Recommendation Trees—0
Towards a Theoretical Framework of Out-of-Distribution Generalization—0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
Towards Better Citation Intent Classification—0
Towards Deep Learning-aided Wireless Channel Estimation and Channel State Information Feedback for 6G—0
Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques—0
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