18567352. LEARNING METHOD, ESTIMATING METHOD, LEARNING DEVICE, ESTIMATING DEVICE, AND PROGRAM simplified abstract (NIPPON TELEGRAPH AND TELEPHONE CORPORATION)
Contents
LEARNING METHOD, ESTIMATING METHOD, LEARNING DEVICE, ESTIMATING DEVICE, AND PROGRAM
Organization Name
NIPPON TELEGRAPH AND TELEPHONE CORPORATION
Inventor(s)
LEARNING METHOD, ESTIMATING METHOD, LEARNING DEVICE, ESTIMATING DEVICE, AND PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18567352 titled 'LEARNING METHOD, ESTIMATING METHOD, LEARNING DEVICE, ESTIMATING DEVICE, AND PROGRAM
Simplified Explanation: A learning device creates a model by maximizing the correlation coefficient between an explanatory variable and an explained variable during learning, then minimizes errors during relearning.
Key Features and Innovation:
- Learning device generates a first learned model by maximizing correlation coefficient between variables.
- Relearning process minimizes errors between explained variables and output from the first learned model.
- Utilizes first and second learning data to improve the accuracy of the learned model.
Potential Applications: This technology can be applied in various fields such as:
- Predictive analytics
- Financial forecasting
- Medical diagnosis
- Weather prediction
- Marketing research
Problems Solved:
- Enhances the accuracy of predictive models
- Improves the efficiency of learning algorithms
- Reduces errors in data analysis
Benefits:
- Increased accuracy in predicting outcomes
- Enhanced decision-making processes
- Improved efficiency in data analysis
Commercial Applications: Title: Advanced Predictive Modeling Technology for Enhanced Decision Making This technology can be utilized in industries such as finance, healthcare, marketing, and meteorology to improve forecasting and decision-making processes.
Prior Art: Readers can explore prior research on machine learning algorithms, predictive modeling, and data analysis techniques to understand the evolution of this technology.
Frequently Updated Research: Stay informed about the latest advancements in machine learning algorithms, predictive modeling techniques, and data analysis methodologies to enhance the application of this technology.
Questions about Predictive Modeling Technology: 1. What are the potential limitations of this technology in real-world applications?
- Answer: The limitations may include data quality issues, overfitting, and interpretability of the models.
2. How does this technology compare to traditional statistical methods in predictive modeling?
- Answer: This technology often outperforms traditional statistical methods by handling complex relationships in data more effectively.
Original Abstract Submitted
A learning device generates a first learned model by subjecting a learning model to learning such that a correlation coefficient between a first explanatory variable for learning and an explained variable output from the learning model is maximized when the learning model that outputs an explained variable in a case where an explanatory variable is input is subjected to learning on the basis of first learning data representing a pair of the first explanatory variable for learning and a first explained variable for learning. When the first learned model is subjected to relearning on the basis of the second learning data representing the pair of the second explanatory variable for learning and the second explained variable for learning, the learning device generates the second learned model by subjecting the first learned model to relearning such that the error between the second explained variable for learning and the explained variable output from the first learned model is minimized.
