Determining confident data samples for machine learning models on unseen data
US11593650B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 21, 2020 |
| Grant date | Feb 28, 2023 |
| Priority date | — |
| Expiry date | May 20, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are provided for determining confident data samples for machine learning (ML) models on unseen data. In one embodiment, a method is provided that comprises extracting, by a system comprising a processor, a feature vector for a data sample based on projection of the data sample onto a standard feature space. The method further comprises processing, by the system, the feature vector using an outlier detection model to determine whether the data sample is within a scope of a training dataset used to train a machine learning model, wherein the outlier detection model was trained using features extracted from the training dataset based on projection of data samples included in the training dataset onto the standard feature space.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.