18105723. SYSTEMS AND METHODS FOR MATRIX OPERATION SELECTOR BASED ON MACHINE LEARNING simplified abstract (Samsung Electronics Co., Ltd.)
Contents
- 1 SYSTEMS AND METHODS FOR MATRIX OPERATION SELECTOR BASED ON MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR MATRIX OPERATION SELECTOR BASED ON MACHINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Matrix Operation Selector
- 1.13 Original Abstract Submitted
SYSTEMS AND METHODS FOR MATRIX OPERATION SELECTOR BASED ON MACHINE LEARNING
Organization Name
Inventor(s)
Hsin-Hsuan Sung of Cary NC (US)
David Thorsley of Morgan Hill CA (US)
Joseph H. Hassoun of Los Gatos CA (US)
SYSTEMS AND METHODS FOR MATRIX OPERATION SELECTOR BASED ON MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18105723 titled 'SYSTEMS AND METHODS FOR MATRIX OPERATION SELECTOR BASED ON MACHINE LEARNING
Simplified Explanation
The patent application describes systems and methods for a matrix operation selector. A selection engine extracts features from a matrix, and a machine learning model selects an action to perform a matrix operation based on the features. The action is predicted to satisfy a reward criterion, and the model is retrained based on the reward.
Key Features and Innovation
- Selection engine extracts features from a matrix
- Machine learning model selects an action for a matrix operation
- Action is predicted to satisfy a reward criterion
- Model is retrained based on the reward
Potential Applications
This technology could be applied in various fields such as:
- Data analysis
- Image processing
- Financial modeling
Problems Solved
- Automates the selection of matrix operations
- Improves efficiency in performing complex operations
- Enhances decision-making processes based on extracted features
Benefits
- Increased accuracy in matrix operations
- Time-saving in selecting appropriate actions
- Adaptive learning and improvement through retraining
Commercial Applications
Title: Advanced Matrix Operation Selector for Enhanced Data Analysis This technology can be utilized in industries such as:
- Finance for predictive modeling
- Healthcare for medical image analysis
- Manufacturing for quality control processes
Prior Art
Readers can explore prior research on machine learning models for matrix operations and feature extraction techniques in data analysis.
Frequently Updated Research
Stay updated on advancements in machine learning algorithms for matrix operations and feature selection methods.
Questions about Matrix Operation Selector
How does the machine learning model determine the action for a matrix operation?
The machine learning model uses extracted features from the matrix to predict an action that satisfies a reward criterion.
What are the potential limitations of using a selection engine for matrix operations?
The limitations could include the complexity of feature extraction and the accuracy of the machine learning model in predicting suitable actions.
Original Abstract Submitted
Systems and methods for matrix operation selector are disclosed. A selection engine receives a matrix as an input and extracts one or more features from the matrix. A machine learning model selects an action based on the one or more features. The action is for performing a matrix operation based on the matrix, and is predicted to satisfy a criterion with respect to a reward. The action is applied for the matrix operation, and a reward is computed based on the applying of the action. The machine learning model is retrained based on the reward.
