Identifying contributing training datasets for outputs of machine learning models
US11887003B1 · kind B1 · utility
Assignees
Inventors
Key dates
| Filing date | May 4, 2018 |
| Grant date | Jan 30, 2024 |
| Priority date | — |
| Expiry date | Dec 1, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for improving a machine learning model are described. In an embodiment, a computing system stores a plurality of training examples comprising training inputs and training outputs. The computing system generates a machine learning model and training the machine learning model using the plurality of training examples. The computing system receives a particular input for the machine learning system and, using the particular input and the machine learning system, computes a particular output. For each training example of the plurality of training examples, the computing system adjusts a weight of the training example on the machine learning system and computes a relative numerical impact on the particular output for the training example, the relative numerical impact reflecting an importance of each training example on the particular output relative to an importance of the other training examples of the plurality of training examples on the particular output. The server computer generates training example relevance data comprising identifiers of the plurality of training examples and the relative numerical impact values for the plurality of training examples. The se…
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.