Non-transitory computer-readable storage medium for storing machine-learning program, machine-learning method, and information processing device

Patent metadata

Field	Value
Publication number	US-2021295156-A1
Application number	US-202117201646-A
Country	US
Kind code	A1
Filing date	Mar 15, 2021
Priority date	Mar 19, 2020
Publication date	Sep 23, 2021
Grant date	—

Abstract

A method includes: generating common information to be commonly applied to plural input data each including a combination of a value of each item and an input value in association with one or more items, the common information being for converting a correspondence between each input value and each input node in a machine learner in a case of inputting the plural input data to the machine learner; generating individual information to be individually applied to each input data, the individual information being for converting the correspondence, in association with a remaining item excluding the one or more items, by using a similarity between test data and collation data obtained by converting the correspondence; generating converted data obtained by converting the correspondence by using the generated common conversion information and the generated individual conversion information; and updating the collation data and the machine learner by using the generated converted data.

First claim

What is claimed is:

1 . A non-transitory computer-readable storage medium for storing a machine-learning program which causes a processor to perform processing, the processing comprising:
generating common conversion information to be commonly applied to a plurality of data, each of the plurality of data including a combination of an item value of each item of a plurality of items and an input value in association with one or more items of the plurality of items, the common conversion information being for converting a correspondence between each of the input values in the plurality of data and each of input nodes in a machine learner in a case of inputting the plurality of data to the machine learner; generating individual conversion information to be individually applied to each of the plurality of data, the individual conversion information being for converting the correspondence of each of the plurality of data, in association with a remaining item excluding the one or more items of the plurality of items, on the basis of a similarity between test data and collation data obtained by converting the correspondence for each of the plurality of data; generating converted data obtained by converting the correspondence of each of the plurality of data on the basis of the generated common conversion information and the generated individual conversion information; and updating the collation data and the learner on the basis of the generated converted data.

2 . The non-transitory computer-readable storage medium according to claim 1 , wherein
the processing of generating common conversion information includes generating the common conversion information on the basis of the similarity between test data and collation data obtained by converting the correspondence for each of the plurality of data.

3 . The non-transitory computer-readable storage medium according to claim 1 , wherein
the updating processing includes further updating the common conversion information on the basis of the generated converted data.

4 . The non-transitory computer-readable storage medium according to claim 1 , wherein
the similarity is expressed by an inner product of a first vector in which input values in the test data are arranged and a second vector in which input values in the collation data are arranged.

5 . The non-transitory computer-readable storage medium according to claim 1 , the processing further comprising:
calculating, for each of the plurality of data, by error back propagation, an error vector in which errors of input values in the converted data generated from the data in a case of inputting the converted data generated from the data to the learner are arranged; and calculating, for each of the plurality of data, a variation vector in which differences in input values between the converted data generated from the data and another converted data generated from the data in a case of varying the common conversion information or the collation data are arranged, wherein the updating processing includes updating the collation data and the learner on the basis of the error vector and the variation vector.

6 . The non-transitory computer-readable storage medium according to claim 1 , the processing further comprising:
generating converted data obtained by converting the correspondence of data to be classified on the basis of the generated common conversion information and the updated collation data, and inputting the converted data to the updated learner; and classifying the data to be classified on the basis of output data output from the learner in response to the input of the converted data to the learner.

7 . A machine-learning method implemented by a computer, the method comprising:
generating common conversion information to be commonly applied to a plurality of data, each of the plurality of data including a combination of an item value of each item of a plurality of items and an input value in association with one or more items of the plurality of items, the common conversion information being for converting a correspondence between each of the input values in the plurality of data and each of input nodes in a machine learner in a case of inputting the plurality of data to the machine learner; generating individual conversion information to be individually applied to each of the plurality of data, the individual conversion information being for converting the correspondence of each of the plurality of data, in association with a remaining item excluding the one or more items of the plurality of items, on the basis of a similarity between test data and collation data obtained by converting the correspondence for each of the plurality of data; generating converted data obtained by converting the correspondence of each of the plurality of data on the basis of the generated common conversion information and the generated individual conversion information; and updating the collation data and the learner on the basis of the generated converted data.

8 . An information processing device comprising
a memory; and a processor circuit coupled to the memory, the processor circuit being configured to: generate common conversion information to be commonly applied to a plurality of data, each of the plurality of data including a combination of an item value of each item of a plurality of items and an input value in association with one or more items of the plurality of items, the common conversion information being for converting a correspondence between each of the input values in the plurality of data and each of input nodes in a machine learner in a case of inputting the plurality of data to the machine learner; generate individual conversion information to be individually applied to each of the plurality of data, the individual conversion information being for converting the correspondence of each of the plurality of data, in association with a remaining item excluding the one or more items of the plurality of items, on the basis of a similarity between test data and collation data obtained by converting the correspondence for each of the plurality of data; generate converted data obtained by converting the correspondence of each of the plurality of data on the basis of the generated common conversion information and the generated individual conversion information; and update the collation data and the learner on the basis of the generated converted data.

Assignees

• Fujitsu Ltd

Inventors

• Maruhashi Koji

Classifications

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/084 Primary

  Backpropagation, e.g. using gradient descent · CPC title

• G06N3/08 Primary

  Learning methods · CPC title