18612275. PROCESS FOR CONTROLLING CONTINUOUSLY LEARNING MODELS simplified abstract (GE Precision Healthcare LLC)
Contents
PROCESS FOR CONTROLLING CONTINUOUSLY LEARNING MODELS
Organization Name
Inventor(s)
[[:Category:Heikki Paavo Aukusti V��n�nen of Espoo (FI)|Heikki Paavo Aukusti V��n�nen of Espoo (FI)]][[Category:Heikki Paavo Aukusti V��n�nen of Espoo (FI)]]
PROCESS FOR CONTROLLING CONTINUOUSLY LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18612275 titled 'PROCESS FOR CONTROLLING CONTINUOUSLY LEARNING MODELS
The subject disclosure pertains to a method of controlling the advancement of a continuous learning model as new versions of the model are generated through the continuous learning process.
- Update component that updates an original algorithm to a new algorithm based on expert analysis of an outcome of the original algorithm
- Execution component that runs both new and original algorithms together against unlabeled data, raising outcome differences for expert review
- Comparison component that evaluates counts of correct outputs and similarities between new and original algorithms to determine if a new model should replace an original model
Potential Applications: - Machine learning systems - Artificial intelligence development - Data analysis and prediction models
Problems Solved: - Ensuring continuous improvement of learning models - Streamlining the process of updating algorithms - Enhancing the accuracy and efficiency of machine learning systems
Benefits: - Improved performance of learning models - Faster adaptation to changing data patterns - Enhanced decision-making capabilities
Commercial Applications: Title: "Enhanced Machine Learning Model Control System" This technology can be utilized in industries such as finance, healthcare, and marketing for more accurate predictions and data analysis, leading to better decision-making and improved outcomes.
Prior Art: Researchers can explore existing patents and publications related to machine learning model control systems, algorithm updates, and continuous learning processes to gain insights into prior art in this field.
Frequently Updated Research: Stay informed about the latest advancements in machine learning algorithms, continuous learning methodologies, and data analysis techniques to enhance the effectiveness of this technology.
Questions about Machine Learning Model Control System: 1. How does the comparison component determine if a new model should replace an original model? The comparison component evaluates counts of correct outputs and similarities between new and original algorithms to make this determination.
2. What are the key benefits of using an update component in a continuous learning model? The update component ensures that the model stays current and adapts to new data patterns, leading to improved performance and accuracy.
Original Abstract Submitted
The subject disclosure relates generally to a method of controlling advancement of a continuous learning model as new versions of the model are generated through the continuous learning process. an update component that updates an original algorithm to a new algorithm based on expert analysis of an outcome of the original algorithm; and an execution component that runs both new and original algorithms together against unlabeled data, wherein outcome differences between the new and original algorithms are raised for expert review; and a comparison component that compares new and original model algorithm performances by evaluating counts corresponding to when each of the respective new or original algorithms are correct and when their outputs are similar to determine if a new model should replace an original model.
