18530349. MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA simplified abstract (NEC Corporation)

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MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA

Organization Name

NEC Corporation

Inventor(s)

Anil Goyal of Dossenheim (DE)

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 18530349 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 and task-specific classifiers learned using a boosting algorithm.

  • Leveraging previously learned classifiers from old tasks
  • Learning a distribution of weights over a learning sample
  • Learning task-specific classifiers 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 the following issues:

  • Efficient adaptation to new tasks
  • Continuous learning without forgetting previous knowledge
  • Improved accuracy and performance over time

Benefits

The benefits of this technology include:

  • Enhanced machine learning capabilities
  • Scalability for handling a large number of tasks
  • Adaptability to changing environments

Potential Commercial Applications

The technology could be used in industries such as:

  • E-commerce for personalized product recommendations
  • Finance for fraud detection and risk assessment
  • Healthcare for predictive analytics and patient monitoring

Possible Prior Art

One possible prior art for this technology is the use of ensemble learning methods in machine learning, such as random forests and gradient boosting, which also combine multiple classifiers to improve predictive performance.

Unanswered Questions

How does this method compare to other lifelong machine learning approaches?

This article does not provide a comparison with other lifelong machine learning approaches, such as incremental learning or transfer learning. It would be helpful to understand the specific advantages and limitations of this method in comparison to existing techniques.

What are the computational requirements for implementing this method in real-world applications?

The article does not address the computational resources needed to implement this method in practical scenarios. Understanding the computational complexity and resource constraints could be crucial for assessing the feasibility of deploying this technology in different settings.


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.

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