The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 29, 2022

Filed:

Aug. 30, 2018
Applicant:

Panasonic Intellectual Property Management Co., Ltd., Osaka, JP;

Inventor:

Kyoko Kawaguchi, Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06K 9/62 (2022.01); G06N 20/00 (2019.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06N 20/10 (2019.01); G06N 7/08 (2006.01); G06N 7/00 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06K 9/628 (2013.01); G06K 9/6232 (2013.01); G06K 9/6268 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); G06N 20/00 (2019.01); G06N 7/005 (2013.01); G06N 20/10 (2019.01);
Abstract

A learning apparatus includes a first learning unit, a learning data generator, and a second learning unit. The first learning unit implements a learning process for a first classifier such that a class applicable degree of a candidate class corresponding to a correct answer class becomes the maximum as compared with class applicable degrees of other candidate classes. The learning data generator performs a classification process for a classification target using the first classifier already subjected to the learning process, converts class applicable degrees of a plurality of candidate classes, which are output by the first classifier already subjected to the learning process into a dimensionally compressed value based on a predetermined compression rule, and generates second learning data in which the identification target is associated with the dimensionally compressed value. The second learning unit implements a learning process for a second classifier using the second learning data.


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