18121302. ONLINE LEARNING METHOD AND ONLINE LEARNING DEVICE simplified abstract (TDK Corporation)
Contents
ONLINE LEARNING METHOD AND ONLINE LEARNING DEVICE
Organization Name
Inventor(s)
ONLINE LEARNING METHOD AND ONLINE LEARNING DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18121302 titled 'ONLINE LEARNING METHOD AND ONLINE LEARNING DEVICE
The abstract of the patent application describes an online learning method that involves compressing and expanding the range of possible values of a Kalman gain before and after an update, respectively. The method also includes updating a weight based on the Kalman gain and an error between a training signal and an inference result.
- Compress a range of possible values of a Kalman gain before an update
- Obtain a Kalman gain after the update from the compressed Kalman gain before the update using an expanded Kalman filter method
- Expand the range of possible values of the Kalman gain after the update
- Update a weight by adding a weight before the update to a result obtained by multiplying the Kalman gain with an error between a training signal and an inference result
Potential Applications: - This technology can be applied in online learning systems to improve the accuracy of predictions and inferences. - It can be used in various fields such as finance, healthcare, and autonomous systems for real-time data analysis and decision-making.
Problems Solved: - Enhances the efficiency and accuracy of online learning algorithms. - Helps in reducing errors and improving the overall performance of predictive models.
Benefits: - Improved accuracy in predictions and inferences. - Enhanced performance of online learning systems. - Real-time data analysis and decision-making capabilities.
Commercial Applications: Title: Enhanced Online Learning Method for Improved Predictions This technology can be utilized in financial trading algorithms, medical diagnosis systems, and autonomous vehicles for better decision-making processes and improved outcomes.
Questions about the technology: 1. How does this online learning method compare to traditional learning algorithms? 2. What are the potential limitations of using this method in real-time applications?
Frequently Updated Research: Stay updated on advancements in online learning algorithms and real-time data analysis techniques to enhance the performance of this technology.
Original Abstract Submitted
An online learning method includes: compressing a range of possible values of a Kalman gain before an update; obtaining a Kalman gain after the update from the compressed Kalman gain before the update using an expanded Kalman filter method; expanding the range of possible values of the Kalman gain after the update, and updating a weight by adding a weight before the update to a result obtained by multiplying the Kalman gain in which the range of the possible values of the Kalman gain is expanded by an error between a training signal and an inference result in which a weight before the update is used.
