公开(公告)号：US20240273357A1

公开(公告)日：2024-08-15

申请号：US18438534

申请日：2024-02-12

Applicant: SEIKO EPSON CORPORATION

Inventor： Yuki URUSHIBATA ,  Ryoki WATANABE ,  Hikaru KURASAWA

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: An evaluation method for evaluating target data includes: inputting a plurality of training sets to a vector neural network machine learning model having a plurality of vector neuron layers to train the machine learning model, the training sets including of general-purpose training data having a type different from the target data and a label corresponding to the general-purpose training data; acquiring a reference feature spectrum; acquiring a target feature spectrum; calculating a spectral similarity that is a similarity between the reference feature spectrum and the target feature spectrum; and evaluating the target data using the spectral similarity.

公开(公告)号：US20230186047A1

公开(公告)日：2023-06-15

申请号：US18063897

申请日：2022-12-09

Applicant: SEIKO EPSON CORPORATION

Inventor： Hikaru KURASAWA ,  Yuki URUSHIBATA ,  Ryoki WATANABE ,  Shin NISHIMURA ,  Eiichiro YAMAGUCHI

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/04 ,  G06N3/08

Abstract: An evaluation method for a trained machine learning model includes the steps of (a) inputting evaluation data to the trained machine learning model to generate first explanatory information used for an evaluation of the machine learning model, (b) using a value indicated by each piece of information included in the first explanatory information to generate second explanatory information indicating an evaluation of the trained machine learning model, and (c) outputting the generated second explanatory information.