18530871. MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA simplified abstract (NEC Corporation)
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 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 18530871 titled 'MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA
Simplified Explanation
The method described in the abstract involves lifelong machine learning using boosting, where a distribution of weights is learned over a learning sample for a new task by leveraging previously learned classifiers from old tasks. Task-specific classifiers are then learned for the new task using a boosting algorithm and the distribution of weights over the learning sample, with updates made using the task-specific classifiers.
- Leveraging previously learned classifiers from old tasks
- Learning task-specific classifiers for new tasks using a boosting algorithm
- Updating the distribution of weights over the learning sample using task-specific classifiers
Potential Applications
The technology could be applied in various fields such as:
- Predictive analytics
- Personalized recommendations
- Fraud detection
Problems Solved
This technology addresses challenges such as:
- Continual learning without forgetting previous tasks
- Efficient adaptation to new tasks
- Improved accuracy over time
Benefits
The benefits of this technology include:
- Enhanced performance on new tasks
- Reduced training time for new tasks
- Scalability for lifelong learning scenarios
Potential Commercial Applications
Potential commercial applications of this technology include:
- E-commerce platforms
- Financial institutions
- Healthcare systems
Possible Prior Art
One possible prior art for this technology is the use of ensemble learning methods in machine learning, where multiple models are combined to improve overall performance.
What are the limitations of this method in handling extremely large datasets?
The abstract does not mention how the method handles extremely large datasets, which could be a challenge in terms of computational resources and processing time.
How does this method compare to other lifelong learning approaches in terms of adaptability to diverse tasks?
The abstract does not provide a comparison with other lifelong learning approaches in terms of adaptability to diverse tasks, leaving a gap in understanding the method's effectiveness in this aspect.
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.
