COMPUTER-READABLE RECORDING MEDIUM, LEARNING METHOD, AND LEARNING APPARATUS

• US 20180330279A1
• Filed: 05/07/2018
• Published: 11/15/2018
• Est. Priority Date: 05/12/2017
• Status: Active Application

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: IWAKURA, Tomoya
• Application Number:
  US15/973,035
• Publication Number:
  US 20180330279A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/08 Learning methods

Abstract

A non-transitory computer-readable recording medium stores a learning program that causes a computer to execute a process including: acquiring learning data that is a learning object for a model in which data and confidence of the data are associated with each other; determining whether learning of the learning data is needed by comparing a predetermined condition with a decision result related to updating of the model accumulated for the learning data acquired at the acquiring; and excluding, from a learning object, the learning data of which learning is determined to be unneeded at the determining.

Claims

1. A non-transitory computer-readable recording medium having stored therein a learning program that causes a computer to execute a process comprising:

acquiring learning data that is a learning object for a model in which data and confidence of the data are associated with each other;

determining whether learning of the learning data is needed by comparing a predetermined condition with a decision result related to updating of the model accumulated for the learning data acquired at the acquiring; and

excluding, from a learning object, the learning data of which learning is determined to be unneeded at the determining.

2. The non-transitory computer-readable recording medium according to claim 1, wherein the process further comprises:

deciding whether learning data to be cross-checked is data used for updating the model, by cross-checking, with the model, the learning data of which learning is determined to be needed at the determining;

updating the model based on the learning data when the learning data to be cross-checked is decided as data used for updating the model at the deciding; and

accumulating a decision result for the learning data to be cross-checked at the deciding.

3. The non-transitory computer-readable recording medium according to claim 2, wherein

the learning data includes a label of a positive instance or a negative instance and a feature amount,

the model learns learning data including a label against confidence of the model as a wrong instance, and

the deciding includes deciding the learning data to be cross-checked as data used for updating the model when the learning data to be cross-checked includes a label against the confidence of the model, and deciding the learning data to be cross-checked as not data used for updating the model when the learning data to be cross-checked includes a label corresponding to the confidence of the model.

4. The non-transitory computer-readable recording medium according to claim 3, wherein

the accumulating includes accumulating a correct classification count indicating a count for correctly classified instances for the learning data decided as not data used for updating the model at the deciding, and

the determining includes determining learning of the learning data to be unneeded, when the correct classification count accumulated for the learning data acquired at the acquiring is equal to or greater than a predetermined threshold.

5. The non-transitory computer-readable recording medium according to claim 3, wherein

the accumulating includes accumulating a correct classification score indicating reliability for correct classification for the learning data decided as not data used for updating the model at the deciding, and

the determining includes determining learning of the learning data to be unneeded, when the correct classification score accumulated for the learning data acquired at the acquiring is equal to or greater than a predetermined threshold.

6. The non-transitory computer-readable recording medium according to claim 3, wherein

the accumulating includes accumulating a correct classification count indicating a count for correctly classified instances for the learning data decided as not data used for updating the model at the deciding, and

the determining includes determining learning of the learning data to be unneeded, when a ratio with respect to a processing count of the correct classification count accumulated for the learning data acquired at the acquiring is equal to or greater than a predetermined threshold.

7. The non-transitory computer-readable recording medium according to claim 3, wherein the process further comprises:

resetting the decision result accumulated for the learning data, when the learning data to be cross-checked is decided as data used for updating the model at the deciding.

8. The non-transitory computer-readable recording medium according to claim 1, wherein

the learning data is a text, and

the acquiring includes acquiring a feature included in the text as the learning object.

9. A learning method comprising:

acquiring learning data that is a learning object for a model in which data and confidence of the data are associated with each other, using a processor;

determining whether learning of the learning data is needed by comparing a predetermined condition with a decision result related to updating of the model accumulated for the learning data acquired at the acquiring, using the processor; and

excluding, from a learning object, the learning data of which learning is determined to be unneeded at the determining, using the processor.

10. A learning apparatus comprising:

a memory; and

a processor coupled to the memory, wherein the processor executes a process comprising;

acquiring learning data that is a learning object for a model in which data and confidence of the data are associated with each other;

determining whether learning of the learning data is needed by comparing a predetermined condition with a decision result related to the model for the learning data accumulated for the learning data acquired at the acquiring; and

excluding, from a learning object, the learning data of which learning is determined to be unneeded.