View source for Nec corporation (20240127087). MACHINE LEARNING KNOWLEDGE MANAGEMENT BASED ON LIFELONG BOOSTING IN PRESENCE OF LESS DATA simplified abstract

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


==Organization Name==

[[:Category:nec corporation|nec corporation]]


[[Category:nec corporation]]

==Inventor(s)==

[[:Category:Anil Goyal of Dossenheim (DE)|Anil Goyal of Dossenheim (DE)]][[Category:Anil Goyal of Dossenheim (DE)]]

[[:Category:Ammar Shaker of Heidelberg (DE)|Ammar Shaker of Heidelberg (DE)]][[Category:Ammar Shaker of Heidelberg (DE)]]

[[:Category:Francesco Alesiani of Heidelberg (DE)|Francesco Alesiani of Heidelberg (DE)]][[Category: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.



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