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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 1051–1060 of 2050 papers

TitleStatusHype
Belief propagation for permutations, rankings, and partial orders—0
The Infinite Contextual Graph Markov Model—0
Gradient-based Hyperparameter Optimization without Validation Data for Learning fom Limited Labels—0
On the Uncomputability of Partition Functions in Energy-Based Sequence Models—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
Probability Distribution on Full Rooted Trees—0
Towards trustworthy explanations with gradient-based attribution methods—0
The supremum principle selects simple, transferable models—0
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language—0
Automatic Componentwise Boosting: An Interpretable AutoML System—0
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