Systems and methods for an adaptive sampling of unlabeled data samples for constructing an informative training data corpus that improves a training and predictive accuracy of a machine learning model
US11496501B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 10, 2022 |
| Grant date | Nov 8, 2022 |
| Priority date | — |
| Expiry date | Jun 10, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L63/1433
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
A system and method for adaptively sampling a corpus of data samples for improving an accuracy of a predictive machine learning model includes: identifying the corpus of data samples, wherein each data sample of the corpus of data samples is associated with a machine learning-derived threat inference value; stratifying the corpus of data samples into a plurality of distinct strata based on the machine learning-derived threat inference value associated with each data sample of the corpus of data samples; adaptively sampling the plurality of distinct strata; constructing a machine learning training corpus comprising a plurality of data samples based on the adaptive sampling of the plurality of distinct strata; and training the predictive machine learning model based on the machine learning training corpus.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.