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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 1111–1120 of 2050 papers

TitleStatusHype
Volumes of logistic regression models with applications to model selection—0
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis—0
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System—0
Wave-shape Function Model Order Estimation by Trigonometric Regression—0
Weighted Leave-One-Out Cross Validation—0
Weighted Likelihood Policy Search with Model Selection—0
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning—0
What are the mechanisms underlying metacognitive learning?—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
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