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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 1331–1340 of 2050 papers

TitleStatusHype
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services—0
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary OrderCode0
Bayesian Optimization for Selecting Efficient Machine Learning Models—0
Additive interaction modelling using I-priorsCode0
The Minimum Description Length Principle for Pattern Mining: A Survey—0
DeepNNK: Explaining deep models and their generalization using polytope interpolationCode0
Prediction in latent factor regression: Adaptive PCR and beyond—0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
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