18210428. MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM simplified abstract (NEC Corporation)
MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
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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 18210428 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 is responsible for moving a sample with a high output error from a target sample group to a source sample group.
- The generation unit then uses the observation data of the samples in both the target and source sample groups to generate a plurality of weak learners.
- Based on these weak learners, the generation unit generates a machine learning model of a certain order.
- The classification error of each weak learner is evaluated using the observation data of the sample in the target sample group after the movement processing.
Potential applications of this technology:
- Improving the accuracy and performance of machine learning models.
- Enhancing predictive capabilities in various fields such as finance, healthcare, and marketing.
- Optimizing decision-making processes in complex systems.
Problems solved by this technology:
- Addressing the issue of high output errors in machine learning models.
- Overcoming limitations in the accuracy and performance of existing models.
- Handling large amounts of observation data efficiently.
Benefits of this technology:
- Increased accuracy and reliability of machine learning models.
- Enhanced predictive capabilities leading to better decision-making.
- Improved efficiency in handling and processing large datasets.
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.
