18279595. LEARNING DEVICE, LEARNING METHOD, AND PROGRAM simplified abstract (Nippon Telegraph and Telephone Corporation)
Contents
- 1 LEARNING DEVICE, LEARNING METHOD, AND PROGRAM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEARNING DEVICE, LEARNING METHOD, AND PROGRAM - 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 Learning Device
- 1.13 Original Abstract Submitted
LEARNING DEVICE, LEARNING METHOD, AND PROGRAM
Organization Name
Nippon Telegraph and Telephone Corporation
Inventor(s)
LEARNING DEVICE, LEARNING METHOD, AND PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18279595 titled 'LEARNING DEVICE, LEARNING METHOD, AND PROGRAM
Simplified Explanation
The learning device described in the patent application divides training data sets based on attribute information and creates new models through iterative learning processes.
- The data set division unit divides training data into multiple sets.
- The divided data set learning unit creates new models by learning from the divided data sets.
- The device improves models by repeating the learning process with different data sets.
Key Features and Innovation
- Division of training data sets based on attribute information.
- Creation of new models through iterative learning processes.
- Improvement of models by learning from multiple data sets.
Potential Applications
This technology can be applied in various fields such as machine learning, data analysis, and predictive modeling.
Problems Solved
This technology addresses the need for efficient model learning from diverse training data sets.
Benefits
- Enhanced model accuracy through iterative learning.
- Improved adaptability to different data sets.
- Increased efficiency in model creation and learning.
Commercial Applications
- Predictive analytics software for businesses.
- Machine learning tools for research institutions.
- Data analysis platforms for various industries.
Prior Art
No prior art information is provided in the abstract.
Frequently Updated Research
There is no information on frequently updated research related to this technology.
Questions about Learning Device
Question 1
How does the device determine the division of training data sets based on attribute information?
The device uses attribute information to divide the training data sets into multiple subsets for more effective learning.
Question 2
What is the advantage of creating new models through iterative learning processes?
Iterative learning allows the device to continuously improve models by learning from different data sets, leading to enhanced accuracy and adaptability.
Original Abstract Submitted
A learning device () according to the present disclosure includes a data set division unit () as a training data processing unit and a divided data set learning unit () as a model learning unit. The data set division unit () divides a new training data set into a plurality of divided data sets on the basis of attribute information. After performing model learning processing using an existing model as a learning target model, the divided data set learning unit () creates a new model by repeating the model learning processing until all the divided data sets are learned using a learned model created by the model learning processing as a new learning target model.
