18270812. TRAINING APPARATUS, CLASSIFICATION APPARATUS, TRAINING METHOD, CLASSIFICATION METHOD, AND PROGRAM simplified abstract (NEC Corporation)
Contents
- 1 TRAINING APPARATUS, CLASSIFICATION APPARATUS, TRAINING METHOD, CLASSIFICATION METHOD, AND PROGRAM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING APPARATUS, CLASSIFICATION APPARATUS, TRAINING METHOD, CLASSIFICATION METHOD, AND PROGRAM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
TRAINING APPARATUS, CLASSIFICATION APPARATUS, TRAINING METHOD, CLASSIFICATION METHOD, AND PROGRAM
Organization Name
Inventor(s)
TRAINING APPARATUS, CLASSIFICATION APPARATUS, TRAINING METHOD, CLASSIFICATION METHOD, AND PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18270812 titled 'TRAINING APPARATUS, CLASSIFICATION APPARATUS, TRAINING METHOD, CLASSIFICATION METHOD, AND PROGRAM
Simplified Explanation
The patent application aims to create an efficient and stable training process, even with limited labeled data in the target domain.
- The innovation focuses on optimizing the training process for machine learning models when only a small amount of labeled data is available in the target domain.
- By developing techniques to leverage existing data and transfer knowledge from related domains, the system can effectively train models with minimal labeled data.
- The process ensures stability and accuracy in model training, even when faced with data scarcity in the target domain.
Potential Applications
This technology can be applied in various industries and fields where labeled data is limited, such as healthcare, finance, and cybersecurity.
Problems Solved
1. Overcoming the challenge of training machine learning models with insufficient labeled data. 2. Improving the efficiency and effectiveness of model training in scenarios with data scarcity.
Benefits
1. Enables the development of accurate machine learning models with limited labeled data. 2. Enhances the stability and reliability of model training processes. 3. Facilitates the application of machine learning in domains with data constraints.
Original Abstract Submitted
To provide an efficient and stable training process even in a case where a small amount of target domain labeled data is available.
