LEARNING MULTIPLE TASKS WITH BOOSTED DECISION TREES
First Claim
1. An apparatus comprising:
- an electronic data processing device configured to perform a multi-task machine learning method to generate a multi-task (MT) predictor for a set of N classification tasks where N is greater than or equal to two, the machine learning method including;
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 N classification tasks; and
constructing the MT predictor based on one or more learned MT-DTs.
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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.
21 Citations
23 Claims
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1. An apparatus comprising:
an electronic data processing device configured to perform a multi-task machine learning method to generate a multi-task (MT) predictor for a set of N classification tasks where N is greater than or equal to two, the machine learning method including; 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 N classification tasks; and constructing the MT predictor based on one or more learned MT-DTs. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method comprising:
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learning a multi-task decision tree (MT-DT) for a set of tasks including at least two tasks, the learning including learning decision rules for nodes of the MT-DT that maximize the largest single-task information gain (IG) for tasks of the set of tasks; and constructing a multi-task (MT) predictor based on one or more learned MT-DTs; wherein the learning and the constructing are performed by an electronic data processing device. - View Dependent Claims (13, 14, 15, 16, 17)
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- 18. A non-transitory storage medium storing instructions executable by an electronic data processing device to perform a method including (i) learning a set of multi-task decision trees (MT-DT'"'"'s) for a set of tasks including at least two tasks using different subsets of a training set wherein the learning of each MT-DT includes learning decision rules for nodes of the MT-DT that maximize an aggregate information gain (IG) that aggregates single-task IG values for tasks of the set of tasks and (ii) constructing a multi-task (MT) predictor as a weighted combination of outputs of the learned set of MT-DT'"'"'s.
Specification