Multi-task machine learning using features bagging and local relatedness in the instance space
US8954357B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 12, 2011 |
| Grant date | Feb 10, 2015 |
| Priority date | — |
| Expiry date | Feb 28, 2033 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A multi-task machine learning component learns a set of tasks comprising two or more different tasks based on a set of examples. The examples are represented by features of a set of features. The multi-task machine learning component comprises a digital processing device configured to learn an ensemble of base rules wherein each base rule is learned for a sub-set of the set of features and comprises a multi-task decision tree (MT-DT) having nodes comprising decision rules for tasks of the set of tasks. An inference component comprises a digital processing device configured to predict a result for at least one task of the set of tasks for an input item represented by features of the set of features using the learned ensemble of base rules.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.