Sequential ensemble model training for open sets
US11526693B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 1, 2020 |
| Grant date | Dec 13, 2022 |
| Priority date | — |
| Expiry date | Sep 23, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/096
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed are systems and method for training an ensemble of machine learning models with a focus on feature engineering. For example, the training of the models encourages each machine learning model of the ensemble to rely on a different set of input features from the training data samples used to train the machine learning models of the ensemble. However, instead of telling each model explicitly which features to learn, in accordance with the disclosed implementations, ML models of the ensemble may be trained sequentially, with each new model trained to disregard input features learned by previously trained ML models of the ensemble and learn based on other features included in the training data samples.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.