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Data · dataset · 2026

Hybrid GPR-DNN Enhanced NSGA-II

Listed in Teesside University Research Data Repository

It constructs a deep learning optimization framework based on Gaussian Process Regression (GPR)-enhanced data augmentation.

Description

This approach leverages the Bayesian inference capabilities of GPR to interpolate between sparse samples, thereby enabling the neural network to capture complex physical features that conventional methods might miss.

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Where it is published

Catalogue records · 1

Topics

Inferred from text
Biomedical engineering 69%
Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
Teesside University Research Data Repositoryoai:data.mendeley.com/277bcf335f.112 d agoJSON v1
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