Nec corporation (20240120099). MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM simplified abstract

MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM

Organization Name

nec corporation

Inventor(s)

Yuki Kosaka of Tokyo (JP)

Shinto Eguchi of Tokyo (JP)

MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240120099 titled 'MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM

Simplified Explanation

The patent application describes a machine learning model generation apparatus that includes a movement unit and a generation unit. The movement unit moves a sample from a target sample group to a source sample group if the output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 exceeds a predetermined amount. The generation unit then generates a plurality of weak learners using observation data from samples in both the target and source sample groups after the movement processing, and creates a t-th order machine learning model based on these weak learners.

• Movement unit performs movement processing of samples based on error threshold
• Generation unit generates weak learners from observation data of samples in target and source groups
• Generation unit creates t-th order machine learning model based on weak learners and evaluation of classification error

Potential Applications

This technology could be applied in various fields such as finance, healthcare, and marketing for improving machine learning model generation processes.

Problems Solved

This technology addresses the issue of high error rates in machine learning models by dynamically adjusting sample groups and generating weak learners to improve model accuracy.

Benefits

- Enhanced accuracy of machine learning models - Dynamic adjustment of sample groups for better model performance - Efficient generation of t-th order machine learning models

Potential Commercial Applications

Optimizing marketing campaigns, improving medical diagnosis accuracy, enhancing financial forecasting models

Possible Prior Art

Prior art may include similar machine learning model generation techniques that involve the use of weak learners and dynamic sample group adjustments.

Unanswered Questions

How does the apparatus handle large datasets during the movement processing?

The patent application does not provide details on how the apparatus manages large datasets when moving samples between target and source sample groups.

What computational resources are required for generating the t-th order machine learning model?

The patent application does not specify the computational resources needed for generating the t-th order machine learning model using the weak learners.

Original Abstract Submitted

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.