18481415. MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM simplified abstract (NEC Corporation)
Contents
- 1 MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
Organization Name
Inventor(s)
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 18481415 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 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 thresholds.
- Generation unit generates weak learners using observation data from target and source sample groups.
- Generation unit creates a t-th order machine learning model based on weak learners and classification error evaluation.
Potential Applications
This technology can be applied in various fields such as finance, healthcare, and marketing for predictive modeling and data analysis.
Problems Solved
1. Efficient generation of machine learning models. 2. Improved accuracy in classification tasks.
Benefits
1. Enhanced model performance. 2. Automated model generation process. 3. Adaptability to changing data distributions.
Potential Commercial Applications
Optimized Machine Learning Model Generation for Enhanced Predictive Analytics
Possible Prior Art
Prior art in machine learning model generation includes ensemble learning methods such as Random Forest and Gradient Boosting.
Unanswered Questions
How does the apparatus handle imbalanced datasets in the sample groups?
The patent application does not specify how the apparatus addresses imbalanced datasets and their impact on model generation.
What computational resources are required for the movement and generation units to operate efficiently?
The patent application does not provide information on the computational resources needed for the movement and generation units to function effectively.
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.
