18069150. GENERATING AN ERROR POLICY FOR A MACHINE LEARNING ENGINE simplified abstract (International Business Machines Corporation)
Contents
GENERATING AN ERROR POLICY FOR A MACHINE LEARNING ENGINE
Organization Name
International Business Machines Corporation
Inventor(s)
Samuel Solomon Ackerman of Haifa (IL)
Eitan Daniel Farchi of Pardes Hana-Karku (IL)
Marcel Zalmanovici of Kiriat Motzkin (IL)
GENERATING AN ERROR POLICY FOR A MACHINE LEARNING ENGINE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18069150 titled 'GENERATING AN ERROR POLICY FOR A MACHINE LEARNING ENGINE
Simplified Explanation:
The patent application describes a computer hardware system that includes a slice generator and a policy generator. The system slices a dataset into multiple parts, combines them based on certain criteria, and generates an error policy using machine learning.
- The system slices a dataset into multiple parts based on true and predicted values of a class variable.
- It combines selected slices by adding observations together until a predetermined number is reached.
- After reaching the predetermined number, an error policy is generated using machine learning.
Key Features and Innovation:
- Slicing and combining datasets based on true and predicted values.
- Generating error policies using machine learning.
- Automating the process of dataset analysis and error policy generation.
Potential Applications:
This technology can be applied in various fields such as data analysis, machine learning, and predictive modeling.
Problems Solved:
This technology addresses the need for efficient dataset analysis and error policy generation in a computer hardware system.
Benefits:
- Streamlined dataset analysis process.
- Improved accuracy in error policy generation.
- Enhanced efficiency in machine learning tasks.
Commercial Applications:
Potential commercial applications include data analytics software, predictive modeling tools, and machine learning platforms.
Questions about the Technology:
1. How does the system determine which slices to combine? 2. What are the advantages of using machine learning for error policy generation?
Frequently Updated Research:
Stay updated on advancements in machine learning algorithms and data analysis techniques relevant to this technology.
Original Abstract Submitted
A computer hardware system includes a slice generator and a policy generator and performs the following. The slice generator slices a first dataset including true values and predicted values of a class variable into a plurality of slices each defining a plurality of observations within the first dataset. A first one and another one of the plurality of slices are selected, and a union of observations is generated by adding observations within the selected another one to observations within the selected first one of the plurality of slices. The selecting another one of the plurality of slices and the generating the union is repeated until a number of observations within the union reaches a predetermined value. Using the policy generator and after the number of observations within the union reaches the predetermined value, an error policy is generated. The predicted values were generated by a machine learning engine.
