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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 1291–1300 of 2050 papers

TitleStatusHype
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
Bayesian Optimization for Selecting Efficient Machine Learning Models—0
Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary OrderCode0
Additive interaction modelling using I-priorsCode0
The Minimum Description Length Principle for Pattern Mining: A Survey—0
Tighter risk certificates for neural networksCode1
Prediction in latent factor regression: Adaptive PCR and beyond—0
DeepNNK: Explaining deep models and their generalization using polytope interpolationCode0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
Extended Stochastic Block Models with Application to Criminal NetworksCode1
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