Scalable online hierarchical meta-learning
AssigneeInternational Business Machines Corporation
InventorsDABY M. SOW, Deepak S. Turaga, Yu Zhang
Life of this patent
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Abstract
A method of meta-learning includes receiving a prediction objective, extracting a plurality of subsets of data from a distributed dataset, generating a plurality of local predictions, wherein each local prediction is based on a different subset of the plurality of subsets of data and the prediction objective, combining the plurality of local predictions, and generating a final prediction based on the combined local predictions.
Claims25
A method of meta-learning, comprising:
- receiving a prediction objective;
- extracting a plurality of subsets of data from a distributed dataset;
- generating a plurality of local predictions, by a plurality of local learners, wherein each local prediction is based on a different subset of the plurality of subsets of data and the prediction objective;
- combining the plurality of local predictions, by a plurality of meta-learners;
- and
- generating a final prediction, by an ensemble learner, based on the combined local predictions.
A method of meta-learning, comprising:
- receiving a prediction objective;
- extracting a plurality of subsets of data, by a plurality of local learners, from a distributed dataset;
- selecting a learning model, by an ensemble learner, for each local learner from among the plurality of local learners;
- updating the learning model of each local learner, by the plurality of local learners, based on at least one learning model of a correlated local learner;
- and
- generating a final prediction, by the ensemble learner, based on data output by each local learner from among the plurality of local learners and the prediction objective.
A computer readable storage medium embodying instructions executed by a processor to perform a method of meta-learning, comprising:
- receiving a prediction objective;
- extracting a plurality of subsets of data from a distributed dataset;
- generating a plurality of local predictions, by a plurality of local learners, wherein each local prediction is based on a different subset of the plurality of subsets of data and the prediction objective;
- combining the plurality of local predictions, by a plurality of meta-learners;
- and
- generating a final prediction, by an ensemble learner, based on the combined local predictions.
A meta-learning system, comprising:
- a plurality of local learners, wherein each local learner is configured to extract a different subset of data from a plurality of subsets of data, and generate a local prediction based on a corresponding subset of data and a prediction objective;
- a plurality of meta-learners, wherein each meta-learner is configured to combine at least two local predictions generated by the plurality of local learners;
- and
- an ensemble learner configured to generate a final prediction based on the combined at least two local predictions.
Dependencies are inferred from the claim text.