Human-in-the-loop interactive model training
US12191007B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 29, 2017 |
| Grant date | Jan 7, 2025 |
| Priority date | — |
| Expiry date | Mar 13, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Example embodiments relate to a method for training a predictive model from data. The method includes defining a multitude of predicates as binary functions operating on time sequences of the features or logical operations on the time sequences of the features. The method also includes iteratively training a boosting model by generating a number of new random predicates, scoring all the new random predicates by weighted information gain with respect to a class label associated with a prediction of the boosting model, selecting a number of the new random predicates with the highest weighted information gain and adding them to the boosting model, computing weights for all the predicates in the boosting model, removing one or more of the selected new predicates with the highest information gain from the boosting model in response to input from an operator. The method may include repeating the prior steps a plurality of times.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.