18699077. MODEL LEARNING DEVICE, METHOD AND PROGRAM simplified abstract (NIPPON TELEGRAPH AND TELEPHONE CORPORATION)
Contents
MODEL LEARNING DEVICE, METHOD AND PROGRAM
Organization Name
NIPPON TELEGRAPH AND TELEPHONE CORPORATION
Inventor(s)
Masahiro Kojima of Musashino-shi, Tokyo (JP)
Yuta Nambu of Musashino-shi, Tokyo (JP)
Yuki Kurauchi of Musashino-shi, Tokyo (JP)
Ryuji Yamamoto of Musashino-shi, Tokyo (JP)
MODEL LEARNING DEVICE, METHOD AND PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18699077 titled 'MODEL LEARNING DEVICE, METHOD AND PROGRAM
Simplified Explanation: The patent application describes a method that involves acquiring learning data from multiple groups, updating hyperparameters and parameters using optimization methods, and estimating optimization parameters to minimize objective functions.
- The method involves acquiring grouped uncoupled data and grouped size comparison data from different groups.
- A hyperparameter is updated using a first optimization method to estimate an optimization hyperparameter that minimizes a first objective function.
- Parameters are updated using a second optimization method based on the acquired data and estimated hyperparameter to estimate an optimization parameter that minimizes a second objective function.
- The estimated optimization parameter is then outputted.
Key Features and Innovation:
- Acquisition of learning data from multiple groups.
- Updating hyperparameters and parameters using optimization methods.
- Estimation of optimization parameters to minimize objective functions.
Potential Applications: The technology can be applied in various fields such as machine learning, data analysis, and optimization algorithms.
Problems Solved: The method addresses the challenges of optimizing parameters in complex systems with grouped data.
Benefits:
- Improved optimization of parameters.
- Enhanced efficiency in data analysis.
- Better performance in machine learning models.
Commercial Applications: Potential commercial uses include improving predictive modeling, optimizing resource allocation, and enhancing decision-making processes in various industries.
Prior Art: Readers can explore prior research on optimization methods in machine learning and data analysis to understand the background of this technology.
Frequently Updated Research: Stay updated on the latest advancements in optimization algorithms and machine learning techniques to enhance the application of this technology.
Questions about the Technology: 1. What are the potential implications of this technology in the field of artificial intelligence? 2. How does this method compare to traditional optimization techniques in terms of efficiency and accuracy?
Original Abstract Submitted
An aspect of the present invention acquires learning data including grouped uncoupled data acquired from a plurality of groups to be investigated, and grouped size comparison data. First, a processing of updating a hyperparameter using a first optimization method is executed on the obtained grouped uncoupled data, and an optimization hyperparameter that minimizes a first objective function is estimated. Next, a processing of updating a parameter using a second optimization method is executed on the basis of the acquired grouped uncoupled data and grouped size comparison data and the estimated optimization hyperparameter, and an optimization parameter that minimizes a second objective function is estimated. Finally, the estimated optimization parameter is outputted.
