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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 971–980 of 2050 papers

TitleStatusHype
AaltoNLP at SemEval-2022 Task 11: Ensembling Task-adaptive Pretrained Transformers for Multilingual Complex NER—0
Lookback for Learning to Branch—0
Best of Both Worlds Model Selection—0
Exploring linguistic feature and model combination for speech recognition based automatic AD detection—0
Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting—0
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution ShiftCode0
Entropy-based Characterization of Modeling Constraints—0
fETSmcs: Feature-based ETS model component selectionCode0
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach—0
Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications—0
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