Multistage learner for efficiently boosting large datasets
First Claim
1. A computer-implemented method comprising:
- receiving a first plurality of examples for training a machine learning system, each example having a respective plurality of features, and each example being received at a respective time;
obtaining data defining a first point in time;
performing a first training iteration by training the machine learning system only on examples having at least one feature initially occurring after the first point in time;
obtaining data defining a second point in time occurring subsequent to the first point in time;
performing a second training iteration by training the machine learning system only on examples having a feature initially occurring after the second point in time; and
performing a third training iteration by training the machine learning system on a second plurality of examples, wherein at least one example of the second plurality has a feature initially occurring after the first point in time, and wherein at least one of the second plurality does not have any features initially occurring after the first point in time.
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Accused Products
Abstract
Implementations of the disclosed subject matter provide methods and systems for using a multistage learner for efficiently boosting large datasets in a machine learning system. A method may include obtaining a first plurality of examples for a machine learning system and selecting a first point in time. Next, a second point in time occurring subsequent to the first point in time may be selected. The machine learning system may be trained using m of the first plurality of examples. Each of the m examples may include a feature initially occurring after the second point in time. In addition, the machine learning system may be trained using n of the first plurality of examples, and each of the n examples may include a feature initially occurring after the first point in time.
3 Citations
17 Claims
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1. A computer-implemented method comprising:
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receiving a first plurality of examples for training a machine learning system, each example having a respective plurality of features, and each example being received at a respective time; obtaining data defining a first point in time; performing a first training iteration by training the machine learning system only on examples having at least one feature initially occurring after the first point in time; obtaining data defining a second point in time occurring subsequent to the first point in time; performing a second training iteration by training the machine learning system only on examples having a feature initially occurring after the second point in time; and performing a third training iteration by training the machine learning system on a second plurality of examples, wherein at least one example of the second plurality has a feature initially occurring after the first point in time, and wherein at least one of the second plurality does not have any features initially occurring after the first point in time. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising:
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one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising; receiving a first plurality of examples for training a machine learning system, each example having a respective plurality of features, and each example being received at a respective time; obtaining data defining a first point in time; performing a first training iteration by training the machine learning system only on examples having at least one feature initially occurring after the first point in time; obtaining data defining a second point in time occurring subsequent to the first point in time; performing a second training iteration by training the machine learning system only on examples having a feature initially occurring after the second point in time; and performing a third training iteration by training the machine learning system on a second plurality of examples, wherein at least one example of the second plurality has a feature initially occurring after the first point in time, and wherein at least one of the second plurality does not have any features initially occurring after the first point in time. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:
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receiving a first plurality of examples for training a machine learning system, each example having a respective plurality of features, and each example being received at a respective time; obtaining data defining a first point in time; performing a first training iteration by training the machine learning system only on examples having at least one feature initially occurring after the first point in time; obtaining data defining a second point in time occurring subsequent to the first point in time; performing a second training iteration by training the machine learning system only on examples having a feature initially occurring after the second point in time; and performing a third training iteration by training the machine learning system on a second plurality of examples, wherein at least one example of the second plurality has a feature initially occurring after the first point in time, and wherein at least one of the second plurality does not have any features initially occurring after the first point in time.
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Specification