公开(公告)号：US20240028912A1

公开(公告)日：2024-01-25

申请号：US17863338

申请日：2022-07-12

Applicant: NEC CORPORATION

Inventor： Vivek BARSOPIA ,  Yoshio KAMEDA ,  Tomoya SAKAI ,  Keita SAKUMA ,  Ryuta MATSUNO

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: Predictively robust models are trained by embedding a distribution of each temporal data set among a plurality of temporal data sets into a feature vector, predicting a future feature vector of a distribution of a future data set, based on the feature vector of each temporal data set among a plurality of temporal data sets, creating the future data set from the future feature vector, perturbing the future data set to produce a plurality of perturbed future data sets, and training a learning function using the future data set and each perturbed future data set to produce a model.

公开(公告)号：US20250094308A1

公开(公告)日：2025-03-20

申请号：US18559113

申请日：2023-03-06

Applicant: NEC Corporation

Inventor： Ryuta MATSUNO ,  Keita SAKUMA

IPC: G06F11/34 ,  G16H50/20

Abstract: In order to provide a model evaluation method in which a plurality of groups are considered, an information processing device according to the present invention includes a data acquisition means that acquires at least one condition for evaluation data of a machine learning model, a performance calculation means that calculates a performance index of the machine learning model and a performance index of the machine learning model after being updated using a data set specified for each of the at least one condition, and an index calculation means that calculates a deterioration index of a performance of the machine learning model based on the performance indexes before and after the machine learning model is updated.

公开(公告)号：US20250077964A1

公开(公告)日：2025-03-06

申请号：US18579531

申请日：2023-08-23

Applicant: NEC Corporation

Inventor： Ryuta MATSUNO ,  Keita SAKUMA

IPC: G06N20/00

Abstract: A model evaluation device 100 of the present disclosure includes a generation unit 121 that generates a plurality of second machine learning models that are different from a first machine learning model subject to performance evaluation, and an evaluation unit 122 that evaluates the first machine learning model on the basis of prediction labels that are output by inputting the same data to the first machine learning model and to each of the second machine learning models. Therefore, the model evaluation device 100 is able to assist decision making by a user.