Learning multiple tasks with boosted decision trees
US8694444B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 20, 2012 |
| Grant date | Apr 8, 2014 |
| Priority date | — |
| Expiry date | Oct 5, 2032 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L51/212
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
A multi-task machine learning method is performed to generate a multi-task (MT) predictor for a set of tasks including at least two tasks. The machine learning method includes: learning a multi-task decision tree (MT-DT) including learning decision rules for nodes of the MT-DT that optimize an aggregate information gain (IG) that aggregates single-task IG values for tasks of the set of tasks; and constructing the MT predictor based on the learned MT-DT. In some embodiments the aggregate IG is the largest single-task IG value of the single-task IG values. In some embodiments the machine learning method includes repeating the MT-DT learning operation for different subsets of a training set to generate a set of learned MT-DT's, and the constructing comprises constructing the MT predictor as a weighted combination of outputs of the set of MT-DT's.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.