Computer-readable recording medium storing machine learning program, machine learning apparatus, and machine learning method

Patent metadata

Field	Value
Publication number	US-2022138627-A1
Application number	US-202117464738-A
Country	US
Kind code	A1
Filing date	Sep 2, 2021
Priority date	Oct 29, 2020
Publication date	May 5, 2022
Grant date	—

Abstract

A machine learning method is performed by a computer. The method includes acquiring first graph information, generating second graph information, without changing a coupling state between nodes included in the first graph information, by a change process of changing an attribute value of a coupling between the nodes, and performing machine learning on a model, based on the first graph information and the second graph information.

First claim

What is claimed is:

1 . A non-transitory computer-readable recording medium storing a machine learning program for causing a computer to execute a process, the process comprising:
acquiring first graph information; generating second graph information, without changing a coupling state between nodes included in the first graph information, by a change process of changing an attribute value of a coupling between the nodes; and performing machine learning on a model, based on the first graph information and the second graph information.

2 . The non-transitory computer-readable recording medium according to claim 1 ,
wherein the without changing the coupling state comprises not adding any new coupling between the nodes included in the first graph information and not deleting any existing coupling between the nodes included in the first graph information.

3 . The non-transitory computer-readable recording medium according to claim 1 ,
wherein the change process includes randomly changing the attribute value.

4 . The non-transitory computer-readable recording medium according to claim 3 ,
wherein the randomly changing the attribute value includes randomly multiplying the attribute value by a value of a specific probability distribution.

5 . The non-transitory computer-readable recording medium according to claim 1 ,
wherein the change process includes multiplying a coefficient corresponding to an appearance frequency for each of specific values or categories associated with the nodes in the first graph information and the attribute value of the coupling including the nodes with which the specific values or categories are associated.

6 . The non-transitory computer-readable recording medium according to claim 5 ,
wherein the coefficient is a relative ratio corresponding to the appearance frequency with respect to a reference value.

7 . The non-transitory computer-readable recording medium according to claim 6 ,
wherein the reference value is an average value or a median of the appearance frequencies.

8 . The non-transitory computer-readable recording medium according to claim 5 ,
wherein the coefficient is a value within a specific range centered at 1.

9 . A machine learning apparatus comprising:
a memory, and a processor coupled to the memory and configured to: acquire first graph information; generate second graph information, without changing a coupling state between nodes included in the first graph information, by a change process of changing an attribute value of a coupling between the nodes; and perform machine learning on a model, based on the first graph information and the second graph information.

10 . The machine learning apparatus according to claim 9 ,
wherein the without changing the coupling state comprises not adding any new coupling between the nodes included in the first graph information and not deleting any existing coupling between the nodes included in the first graph information.

11 . The machine learning apparatus according to claim 9 ,
wherein the change process includes randomly changing the attribute value.

12 . The machine learning apparatus according to claim 11 ,
wherein the randomly changing the attribute value includes randomly multiplying the attribute value by a value of a specific probability distribution.

13 . The machine learning apparatus according to claim 9 ,
wherein the change process includes multiplying a coefficient corresponding to an appearance frequency for each of specific values or categories associated with the nodes in the first graph information and the attribute value of the coupling including the nodes with which the specific values or categories are associated.

14 . The machine learning apparatus according to claim 13 ,
wherein the coefficient is a relative ratio corresponding to the appearance frequency with respect to a reference value.

15 . The machine learning apparatus according to claim 14 ,
wherein the reference value is an average value or a median of the appearance frequencies.

16 . The machine learning apparatus according to claim 13 ,
wherein the coefficient is a value within a specific range center at 1.

17 . A machine learning method performed by a computer, the method comprising:
acquiring first graph information; generating second graph information, without changing a coupling state between nodes included in the first graph information, by a change process of changing an attribute value of a coupling between the nodes; and performing machine learning on a model, based on the first graph information and the second graph information.

18 . The machine learning method according to claim 17 ,
wherein the without changing the coupling state comprises not adding any new coupling between the nodes included in the first graph information and not deleting any existing coupling between the nodes included in the first graph information.

19 . The machine learning apparatus according to claim 18 ,
wherein the change process includes randomly changing the attribute value.

20 . The machine learning method according to claim 19 ,
wherein the randomly changing the attribute value includes processing of randomly multiplying the attribute value by a value of a specific probability distribution.

Assignees

• Fujitsu Ltd

Inventors

• Todoriki Masaru
• Maruhashi Koji

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F18/28

  Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title