Iterative training of computer model for machine learning

The present disclosure relates to a computer receiving a current training dataset. A first fraction of the training dataset comprises synthetic training data and a remaining second fraction of the training dataset comprising real-life training data. The real-life training data is user defined data a...

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Bibliographic Details
Main Authors OBERHOFER, MARTIN ANTON, KOENIG, HOLGER, BREMER, LARS, OEVERS, MANFRED
Format Patent
LanguageChinese
English
Published 11.07.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:The present disclosure relates to a computer receiving a current training dataset. A first fraction of the training dataset comprises synthetic training data and a remaining second fraction of the training dataset comprising real-life training data. The real-life training data is user defined data and the synthetic training data is system defined data. A machine learning based engine is trained and may repeatedly be performed by using the current training dataset. In each iteration or a subset of the iterations, the training dataset is updated by adding real-life training data, thereby increasing the second fraction in the updated training dataset and reducing the first fraction of the synthetic training data.
Bibliography:Application Number: TW20220119026