发明名称 |
Learning multiple tasks with boosted decision trees |
摘要 |
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. |
申请公布号 |
US8694444(B2) |
申请公布日期 |
2014.04.08 |
申请号 |
US201213451816 |
申请日期 |
2012.04.20 |
申请人 |
FADDOUL JEAN-BAPTISTE;CHIDLOVSKII BORIS;XEROX CORPORATION |
发明人 |
FADDOUL JEAN-BAPTISTE;CHIDLOVSKII BORIS |
分类号 |
G06N5/00 |
主分类号 |
G06N5/00 |
代理机构 |
|
代理人 |
|
主权项 |
|
地址 |
|