18399819. LEARNING APPARATUS AND LEARNING METHOD simplified abstract (CANON KABUSHIKI KAISHA)
Contents
LEARNING APPARATUS AND LEARNING METHOD
Organization Name
Inventor(s)
Hiroshi Yoshikawa of Kanagawa (JP)
LEARNING APPARATUS AND LEARNING METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18399819 titled 'LEARNING APPARATUS AND LEARNING METHOD
- Simplified Explanation:
The patent application describes a learning apparatus that assigns different tasks to neural networks and determines whether to assign the same tasks to the same networks in subsequent learning processing.
- Key Features and Innovation:
- Assigns M tasks to N neural networks in parallel processing. - Determines task assignment based on learning results. - Allows for flexibility in task assignment to optimize learning outcomes.
- Potential Applications:
- Machine learning systems - Artificial intelligence development - Data processing and analysis
- Problems Solved:
- Optimizing learning processing for diverse tasks - Enhancing neural network efficiency - Improving overall learning outcomes
- Benefits:
- Enhanced learning efficiency - Flexibility in task assignment - Improved performance of neural networks
- Commercial Applications:
Title: "Optimized Learning Apparatus for Neural Networks" This technology can be used in various industries such as healthcare, finance, and technology for data analysis, pattern recognition, and predictive modeling.
- Questions about the Technology:
1. How does this technology improve the efficiency of learning processing? - The technology optimizes task assignment to neural networks based on learning results, leading to improved overall performance. 2. What are the potential applications of this learning apparatus in real-world scenarios? - The technology can be applied in various industries for data analysis, pattern recognition, and predictive modeling.
Original Abstract Submitted
A learning apparatus performs a first assignment in which M tasks that are different from each other are assigned to N neural networks (where N<M) and perform learning processing that is related to the M tasks in parallel; and determines, based on learning results of the respective M tasks, whether to assign, in subsequent learning processing, the respective M tasks to the same neural networks as in the first assignment or to neural networks different from those of the first assignment.
