18285307. PREDICTION MODEL GENERATION APPARATUS, PREDICTION MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM simplified abstract (NEC Corporation)
Contents
- 1 PREDICTION MODEL GENERATION APPARATUS, PREDICTION MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PREDICTION MODEL GENERATION APPARATUS, PREDICTION MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Prediction Model Generation Apparatus
- 1.13 Original Abstract Submitted
PREDICTION MODEL GENERATION APPARATUS, PREDICTION MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
Organization Name
Inventor(s)
Kosuke Nishihara of Tokyo (JP)
PREDICTION MODEL GENERATION APPARATUS, PREDICTION MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18285307 titled 'PREDICTION MODEL GENERATION APPARATUS, PREDICTION MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
Simplified Explanation
The patent application describes a prediction model generation apparatus that divides a region based on the probability distribution of an objective variable, models the existence probability of the variable in each small region, and constructs a prediction model by integrating the probability distributions for each region.
- The apparatus divides a region into small regions based on the property of the objective variable.
- It models the existence probability of the variable in each small region.
- The learning data is used to model probability distributions related to possible values of the variable in each small region.
- A prediction model is constructed by integrating the modeled probability distributions for each small region using the existence probability.
Key Features and Innovation
- Division of a region based on the probability distribution of an objective variable.
- Modeling of existence probability for each small region.
- Construction of a prediction model by integrating probability distributions for each small region.
Potential Applications
The technology can be applied in various fields such as finance, healthcare, weather forecasting, and marketing for predictive analysis and decision-making.
Problems Solved
The technology addresses the need for accurate prediction models by effectively modeling the probability distributions of objective variables in different regions.
Benefits
- Improved accuracy in prediction models.
- Enhanced decision-making based on integrated probability distributions.
- Efficient analysis of data for better insights.
Commercial Applications
- Financial forecasting models.
- Healthcare diagnosis and treatment prediction.
- Weather prediction systems.
- Marketing strategies based on predictive analytics.
Prior Art
Information on prior art related to this technology is not provided in the abstract.
Frequently Updated Research
There is ongoing research in the field of predictive modeling and probability distribution integration for improved prediction accuracy.
Questions about Prediction Model Generation Apparatus
Question 1
How does the apparatus determine the existence probability of the objective variable in each small region?
The apparatus determines the existence probability by analyzing the learning data and modeling the likelihood of the variable belonging to each small region.
Question 2
What are the potential implications of using this prediction model generation apparatus in the financial sector?
The use of this technology in the financial sector can lead to more accurate forecasting models, better risk assessment, and improved decision-making processes.
Original Abstract Submitted
A prediction model generation apparatus according to an example embodiment of the present disclosure includes: at least one memory storing instructions; and at least one processor configured to execute the instructions to: divide a region in which a probability distribution of an objective variable exists into a plurality of small regions according to a property of the objective variable for learning data including the objective variable; model an existence probability that the objective variable belongs to each of the small regions; use the learning data to model, for each of the small regions, a probability distribution related to a possible value of the objective variable in the small region under a condition that the objective variable belongs to the small region; and constructs a prediction model of the objective variable by integrating the modeled probability distribution for each of the small regions using the existence probability.
