Samsung display co., ltd. (20240127030). MACHINE LEARNING SYSTEMS AND METHODS FOR CLASSIFICATION simplified abstract
Contents
- 1 MACHINE LEARNING SYSTEMS AND METHODS FOR CLASSIFICATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING SYSTEMS AND METHODS FOR CLASSIFICATION - 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 SYSTEMS AND METHODS FOR CLASSIFICATION
Organization Name
Inventor(s)
Qisen Cheng of San Jose CA (US)
Kaushik Balakrishnan of San Jose CA (US)
Janghwan Lee of San Jose CA (US)
MACHINE LEARNING SYSTEMS AND METHODS FOR CLASSIFICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127030 titled 'MACHINE LEARNING SYSTEMS AND METHODS FOR CLASSIFICATION
Simplified Explanation
The patent application describes a classification system that calculates reference Shapley values for features of a data sample based on a first classification model, and then trains a second classification model using multi-task distillation to predict Shapley values for the features of the data sample and predict a class label for the data sample based on the predicted Shapley values and a ground truth class label.
- The system includes one or more processors and memory with instructions for executing the described process.
- The first classification model is used to calculate reference Shapley values for the features of a data sample.
- The second classification model is trained through multi-task distillation to predict Shapley values for the features of the data sample and predict a class label for the data sample.
Potential Applications
This technology could be applied in various fields such as healthcare, finance, and marketing for improving classification models and making more accurate predictions.
Problems Solved
This technology helps in improving the accuracy of classification models by incorporating Shapley values and ground truth class labels in the prediction process.
Benefits
The benefits of this technology include enhanced model performance, better prediction accuracy, and improved decision-making based on the predicted class labels.
Potential Commercial Applications
Potential commercial applications of this technology could include predictive analytics software, automated decision-making systems, and personalized recommendation engines.
Possible Prior Art
One possible prior art could be the use of Shapley values in machine learning models for feature importance analysis and prediction improvement.
Unanswered Questions
How does this technology compare to existing methods for feature importance analysis in classification models?
This article does not provide a direct comparison with existing methods for feature importance analysis in classification models.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not address the potential limitations or challenges of implementing this technology in real-world applications.
Original Abstract Submitted
a classification system includes: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the one or more processors to: calculate reference shapley values for features of a data sample based on a first classification model; and train a second classification model though multi-task distillation to: predict shapley values for the features of the data sample based on the reference shapley values and a distillation loss; and predict a class label for the data sample based on the predicted shapley values and a ground truth class label for the data sample.
