Active Machine Learning

• US 20160162802A1
• Filed: 12/07/2014
• Published: 06/09/2016
• Est. Priority Date: 12/07/2014
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Chickering, David Maxwell, Meek, Christopher A., Simard, Patrice Y., Iyer, Rishabh Krishnan
• Granted Patent: US 10,262,272 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Technologies are described herein for active machine learning. An active machine learning method can include initiating active machine learning through an active machine learning system configured to train an auxiliary machine learning model to produce at least one new labeled observation, refining a capacity of a target machine learning model based on the active machine learning, and retraining the auxiliary machine learning model with the at least one new labeled observation subsequent to refining the capacity of the target machine learning model. Additionally, the target machine learning model is a limited-capacity machine learning model according to the description provided herein.

Claims

1. A method comprising:

initiating active machine learning through an active machine learning system configured to train an auxiliary machine learning model to produce at least one new labeled observation;

refining a target machine learning model based at least on the active machine learning, wherein the target machine learning model includes a limited-capacity machine learning model; and

retraining the auxiliary machine learning model with the at least one new labeled observation subsequent to refining the capacity of the target machine learning model.

2. A method as claim 1 recites, wherein the auxiliary machine learning model includes a capacity larger than the target machine learning model.

3. A method as claim 1 recites, wherein the auxiliary machine learning model includes a semantic machine learning model.

4. A method as claim 3 recites, wherein the semantic machine learning model includes a bag-of-words machine learning model.

5. A method as claim 1 recites, wherein initiating the active machine learning comprises:

selecting one or more unlabeled observations from a pool of unlabeled observations.

6. A method as claim 5 recites, wherein the refining the capacity includes incrementally adding or removing features from the target machine learning model based on an output of the auxiliary machine learning model responsive to processing the one or more unlabeled observations.

7. A method as claim 1 recites, wherein the refining the capacity includes incrementally adding or removing features from the target machine learning model based at least on the initiated active machine learning of the auxiliary machine learning model.

8. A method as claim 1 recites, further comprising implementing diversity in the initiated active machine learning by at least one submodular function.

9. A method as claim 1 recites, further comprising implementing diversity in the initiated active machine learning by establishing a subset labelset in a pool of unlabeled observations configured to provide diverse unlabeled observations from the subset labelset.

10. A method as claim 9 recites, further comprising selecting one or more unlabeled observations from the subset labelset for processing by the auxiliary machine learning model.

11. A method as claim 1 recites, further comprising reducing a color-blindness of the target machine learning model based at least on disagreement between the auxiliary machine learning model and the target machine learning model.

12. A computer-readable medium having computer-executable instructions thereupon that, when executed by a computer, cause the computer to perform operations comprising:

selecting an unlabeled observation from a pool of unlabeled observations through an auxiliary machine learning model, wherein it is not known to which one of a plurality of classes the unlabeled observation belongs;

converting the unlabeled observation to a new labeled observation based on an output of the auxiliary machine learning model responsive to the unlabeled observation;

refining a capacity of a target machine learning model based on the converting, wherein the target machine learning model is a limited-capacity machine learning model; and

retraining the auxiliary machine learning model with the new labeled observation subsequent to refining the capacity of the target machine learning model.

13. A computer-readable medium as claim 12 recites, wherein the refining the capacity includes:

incrementally adding at least one feature to the target machine learning model based on features contained within the new labeled observation; and

incrementally removing at least one feature from the target machine learning model based on the features contained within the new labeled observation.

14. A computer-readable medium as claim 12 recites, wherein the selecting the unlabeled observation includes selecting the unlabeled observations based at least on optimization of at least one submodular function.

15. A computer-readable medium as claim 12 recites, wherein the selecting the unlabeled observation includes selecting the unlabeled observations from a subset labelset in the pool of unlabeled observations, the subset labelset configured to provide diverse unlabeled observations.

16. A computer-readable medium as claim 12 recites, wherein the refining the capacity of the target machine learning model includes reducing a color-blindness of the target machine learning model based at least on disagreement between the auxiliary machine learning model and the target machine learning model.

17. An active machine learning system, the system comprising:

an auxiliary machine learning model configured to assign a first score to an unlabeled observation;

a target machine learning model configured to assign a second score to the unlabeled observation, wherein the target machine learning model and the auxiliary machine learning model are from different machine learning model classes, and wherein the target machine learning model is a limited-capacity machine learning model;

a comparison component configured to compare the first score and the second score to determine a probability that the target machine learning model has returned a false positive or a false negative result; and

a featuring component configured to receive the output of the comparison component.

18. A system as claim 17 recites, wherein the comparison component configured to compare the first score and the second score is further configured to perform comparison comprising:

determining a magnitude of the difference between the first score and the second score;

determining that the target machine learning model has returned a false positive when the magnitude is negative; and

determining that the target machine learning model has returned a false negative when the magnitude is positive.

19. A system as claim 18 recites, further comprising a capacity-refining component in operative communication with the featuring component, the capacity-refining component configured to:

extend a scope of the target machine learning model to include a new feature previously not within the scope of the target machine learning model when the target machine learning model has returned a false positive.

20. A system as claim 18 recites, further comprising a capacity-refining component in operative communication with the featuring component, the capacity-refining component configured to:

narrow a scope of the target machine learning model to remove a feature previously within the scope of the target machine learning model when the target machine learning model has returned a false positive.