Automatic tuning of machine learning parameters for non-stationary e-commerce data
US11521254B2 · kind B2 · utility
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20Claims
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Key dates
| Filing date | Oct 21, 2019 |
| Grant date | Dec 6, 2022 |
| Priority date | — |
| Expiry date | May 18, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0603
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are disclosed for automatically adjusting machine learning parameters in an e-commerce system. Hyperparameters of a machine learning component are tuned using a gradient estimator and a first training set representative of an e-commerce context. The machine learning component is trained using the tuned hyperparameters and the first training set. The hyperparameters are automatically re-tuned using the gradient estimator and a second training set representative of a changed e-commerce context. The machine learning component is re-trained using the re-tuned hyperparameters and the second training set.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.