Nippon telegraph and telephone corporation (20240281656). LEARNING METHOD, ESTIMATING METHOD, LEARNING DEVICE, ESTIMATING DEVICE, AND PROGRAM simplified abstract
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 20240281656 titled 'LEARNING METHOD, ESTIMATING METHOD, LEARNING DEVICE, ESTIMATING DEVICE, AND PROGRAM
The abstract describes a learning device that generates a first learned model by maximizing the correlation coefficient between an explanatory variable and an explained variable output from a learning model. The device then generates a second learned model by minimizing the error between the explained variable for learning and the output from the first learned model when relearning on new data.
- The learning device creates a first learned model by maximizing correlation between input and output variables.
- It then generates a second learned model by minimizing errors between the output and the explained variable for learning.
- The device uses relearning on new data to improve the accuracy of the learned models.
- This process allows for the refinement and optimization of the learning models over time.
- The innovation aims to enhance the accuracy and effectiveness of learning algorithms in various applications.
Potential Applications: - Machine learning systems - Predictive analytics - Data modeling and analysis
Problems Solved: - Improving the accuracy of learning models - Enhancing the performance of predictive algorithms
Benefits: - Increased accuracy in predicting outcomes - Better decision-making based on data analysis - Optimization of learning algorithms for various applications
Commercial Applications: Title: Enhanced Learning Models for Predictive Analytics This technology can be used in industries such as finance, healthcare, and marketing to improve predictive modeling and decision-making processes. It can also be applied in research and development for data analysis and pattern recognition.
Questions about the technology: 1. How does this innovation improve the accuracy of learning models? - The technology optimizes learning models by maximizing correlation and minimizing errors between input and output variables. 2. What are the potential applications of this enhanced learning device? - The device can be used in various industries for predictive analytics, data modeling, and decision-making processes.
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.
