18592940. MEASURING THE GENERALIZATION ABILITY OF A TRAINED MACHINE LEARNING MODEL WITH RESPECT TO GIVEN MEASUREMENT DATA simplified abstract (Robert Bosch GmbH)
MEASURING THE GENERALIZATION ABILITY OF A TRAINED MACHINE LEARNING MODEL WITH RESPECT TO GIVEN MEASUREMENT DATA
Organization Name
Inventor(s)
Anna Khoreva of Stuttgart (DE)
MEASURING THE GENERALIZATION ABILITY OF A TRAINED MACHINE LEARNING MODEL WITH RESPECT TO GIVEN MEASUREMENT DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18592940 titled 'MEASURING THE GENERALIZATION ABILITY OF A TRAINED MACHINE LEARNING MODEL WITH RESPECT TO GIVEN MEASUREMENT DATA
The abstract describes a method for evaluating the ability of a trained machine learning model to generalize to a target domain based on measurement data.
- Determining the target style from input measurement data records that characterize the target domain.
- Obtaining validation examples and ground truth labels in the target domain based on the target style.
- Processing the validation examples using the trained machine learning model to generate outputs.
- Evaluating the accuracy of the model by comparing the outputs with the ground truth labels to assess its generalization ability to the target domain.
- Potential Applications:
This technology can be applied in various fields such as healthcare, finance, and marketing where accurate data processing and generalization are crucial.
- Problems Solved:
This method addresses the challenge of determining the generalization ability of a machine learning model to a specific target domain, ensuring reliable performance in real-world applications.
- Benefits:
- Improved accuracy in processing measurement data - Enhanced performance of machine learning models in diverse domains - Increased reliability and efficiency in data analysis tasks
- Commercial Applications:
The technology can be utilized in industries such as healthcare for patient diagnosis, finance for fraud detection, and marketing for customer segmentation, leading to more effective decision-making processes.
- Prior Art:
Researchers can explore existing literature on machine learning model evaluation and generalization techniques to further enhance this method.
- Frequently Updated Research:
Stay updated on advancements in machine learning model evaluation techniques and applications in various industries to leverage the latest innovations in data processing and analysis.
- Questions about machine learning model generalization:
1. How does the method determine the target style from input measurement data records?
- The target style is determined by analyzing the characteristics of the measurement data records to identify patterns specific to the target domain.
2. What are the key factors that influence the accuracy of the trained machine learning model in generalizing to the target domain?
- The accuracy of the model is influenced by the quality of the validation examples, the relevance of the target style, and the consistency between the model outputs and ground truth labels.
Original Abstract Submitted
A method for measuring the ability of a trained machine learning model for the processing of measurement data to generalize, with respect to a given task, to a target domain and/or distribution to which one or more input records of measurement data belong. The method includes: determining), from the input records of measurement data, a target style that characterizes the target domain and/or distribution; obtaining, based at least in part on the target style, validation examples in the target domain and/or distribution, and also corresponding ground truth labels; processing, by the trained machine learning model, the validation examples into outputs; and determining, based on a comparison between the outputs and the respective ground truth labels, the accuracy of the trained machine learning model as the sought ability of the trained machine learning model to generalize to the target domain.
