18481607. 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 18481607 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 performs movement processing by transferring 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 generates a plurality of weak learners using observation data from samples in the target sample group and the source sample group after the movement processing. It then generates a t-th order machine learning model based on these weak learners, evaluating the classification error for each weak learner using observation data at time t from the sample in the target sample group after the movement processing.
Potential applications of this technology:
- Machine learning model generation for various tasks such as image recognition, natural language processing, and predictive analytics.
- Adaptive machine learning models that can adjust and improve their performance over time.
Problems solved by this technology:
- Overcoming the limitations of a single machine learning model by generating a higher-order model that combines multiple weak learners.
- Addressing the issue of output errors exceeding a predetermined threshold by transferring samples between target and source sample groups.
Benefits of this technology:
- Improved accuracy and performance of machine learning models by generating higher-order models.
- Adaptability to changing data patterns and improving model performance over time.
- Efficient utilization of observation data by transferring samples between target and source sample groups.
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.
