Nec corporation (20240127087). MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA simplified abstract
Contents
- 1 MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA
Organization Name
Inventor(s)
Ammar Shaker of Heidelberg (DE)
Francesco Alesiani of Heidelberg (DE)
MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127087 titled 'MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA
Simplified Explanation
The abstract describes a method for lifelong machine learning using boosting, which involves learning a distribution of weights over a learning sample for a new task by utilizing previously learned classifiers from old tasks and task-specific classifiers learned for the new task using a boosting algorithm.
- Receiving a new task and a learning sample for the new task
- Learning a distribution of weights over the learning sample using previously learned classifiers from old tasks
- Learning task-specific classifiers for the new task using a boosting algorithm and the distribution of weights over the learning sample
- Updating the distribution of weights over the learning sample using the task-specific classifiers for the new task
Potential Applications
This technology could be applied in various fields such as:
- Autonomous vehicles
- Healthcare diagnostics
- Financial forecasting
Problems Solved
This technology addresses the following issues:
- Adapting to new tasks without forgetting previous knowledge
- Improving the efficiency of machine learning models
- Enhancing the accuracy of predictions over time
Benefits
The benefits of this technology include:
- Continuous learning without the need for retraining from scratch
- Increased adaptability to changing environments
- Enhanced performance on a wide range of tasks
Potential Commercial Applications
A potential commercial application of this technology could be:
- Developing personalized recommendation systems for e-commerce platforms
Possible Prior Art
One possible prior art for this technology could be:
- Incremental learning algorithms used in online advertising platforms
Unanswered Questions
How does this method compare to traditional machine learning approaches?
This article does not provide a direct comparison between this method and traditional machine learning approaches.
What are the computational requirements of implementing this method?
The article does not delve into the computational resources needed to implement this method effectively.
Original Abstract Submitted
a method for lifelong machine learning using boosting includes receiving a new task and a learning sample for the new task. a distribution of weights is learned over the learning sample using previously learned classifiers from old tasks. a set of task-specific classifiers are learned for the new task using a boosting algorithm and the distribution of weights over the learning sample, whereby the distribution of weights over the learning sample is updated using the task-specific classifiers for the new task.
