View source for 18459320. TRAINING ENSEMBLE MODELS TO IMPROVE PERFORMANCE IN THE PRESENCE OF UNRELIABLE BASE CLASSIFIERS simplified abstract (Western Digital Technologies, Inc.)

=TRAINING ENSEMBLE MODELS TO IMPROVE PERFORMANCE IN THE PRESENCE OF UNRELIABLE BASE CLASSIFIERS=


==Organization Name==

[[:Category:Western Digital Technologies, Inc.|Western Digital Technologies, Inc.]]


[[Category:Western Digital Technologies, Inc.]]

==Inventor(s)==

[[:Category:Yongjune Kim of San Jose CA (US)|Yongjune Kim of San Jose CA (US)]][[Category:Yongjune Kim of San Jose CA (US)]]

[[:Category:Yuval Cassuto of Sunnyvale CA (US)|Yuval Cassuto of Sunnyvale CA (US)]][[Category:Yuval Cassuto of Sunnyvale CA (US)]]

==TRAINING ENSEMBLE MODELS TO IMPROVE PERFORMANCE IN THE PRESENCE OF UNRELIABLE BASE CLASSIFIERS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18459320 titled 'TRAINING ENSEMBLE MODELS TO IMPROVE PERFORMANCE IN THE PRESENCE OF UNRELIABLE BASE CLASSIFIERS

==Simplified Explanation==

The abstract of the patent application describes a system and method for training base classifiers in a boosting algorithm. The system optimally trains base classifiers considering an unreliability model and then uses an aggregator decoder that reverse-flips inputs using inter-classifier redundancy introduced in training.

* The system and method are used for training base classifiers in a boosting algorithm.
* The training process considers an unreliability model to optimize the training of the base classifiers.
* An aggregator decoder is used to reverse-flip inputs using inter-classifier redundancy introduced during training.

== Potential Applications ==
* Machine learning and artificial intelligence systems
* Pattern recognition systems
* Data analysis and prediction models

== Problems Solved ==
* Improves the training process of base classifiers in a boosting algorithm
* Addresses the issue of unreliability in training base classifiers
* Enhances the accuracy and reliability of the boosting algorithm

== Benefits ==
* Optimal training of base classifiers considering an unreliability model
* Improved accuracy and reliability of the boosting algorithm
* Enhanced performance of machine learning and pattern recognition systems.


==Original Abstract Submitted==

A system and method for training base classifiers in a boosting algorithm includes optimally training base classifiers considering an unreliability model, and then using a scheme with an aggregator decoder that reverse-flips inputs using inter-classifier redundancy introduced in training.



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