公开(公告)号：US20230420129A1

公开(公告)日：2023-12-28

申请号：US18210428

申请日：2023-06-15

Applicant: NEC Corporation

Inventor： Yuki KOSAKA ,  Shinto Eguchi

IPC: G16H50/20

CPC classification number: G16H50/20

Abstract: A machine learning model generation apparatus includes: a movement unit that performs movement processing of moving a sample, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 being larger than a predetermined amount, from the target sample group to a source sample group; and a generation unit that generates a plurality of weak learners by using at least observation data of a sample included in the target sample group after the movement processing and a sample included in the source sample group after the movement processing, and generates a t-th order machine learning model, based on at least each of the plurality of weak learners, and a classification error being evaluated, for each of the plurality of weak learners, by using observation data at time t of the sample included in the target sample group after the movement processing.

公开(公告)号：US20240047068A1

公开(公告)日：2024-02-08

申请号：US18481426

申请日：2023-10-05

Applicant: NEC Corporation

Inventor： Yuki KOSAKA ,  Shinto Eguchi

IPC: G16H50/20

CPC classification number: G16H50/20

Abstract: A machine learning model generation apparatus includes: a movement unit that performs movement processing of moving a sample, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 being larger than a predetermined amount, from the target sample group to a source sample group; and a generation unit that generates a plurality of weak learners by using at least observation data of a sample included in the target sample group after the movement processing and a sample included in the source sample group after the movement processing, and generates a t-th order machine learning model, based on at least each of the plurality of weak learners, and a classification error being evaluated, for each of the plurality of weak learners, by using observation data at time t of the sample included in the target sample group after the movement processing.