18588748. DETERMINING WHETHER A GIVEN INPUT RECORD OF MEASUREMENT DATA IS COVERED BY THE TRAINING OF A TRAINED MACHINE LEARNING MODEL simplified abstract (Robert Bosch GmbH)
DETERMINING WHETHER A GIVEN INPUT RECORD OF MEASUREMENT DATA IS COVERED BY THE TRAINING OF A TRAINED MACHINE LEARNING MODEL
Organization Name
Inventor(s)
Anna Khoreva of Stuttgart (DE)
DETERMINING WHETHER A GIVEN INPUT RECORD OF MEASUREMENT DATA IS COVERED BY THE TRAINING OF A TRAINED MACHINE LEARNING MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18588748 titled 'DETERMINING WHETHER A GIVEN INPUT RECORD OF MEASUREMENT DATA IS COVERED BY THE TRAINING OF A TRAINED MACHINE LEARNING MODEL
- Simplified Explanation:**
This patent application describes a method for determining if a given input record of measurement data belongs to the domain and distribution of training examples used to train a machine learning model.
- Key Features and Innovation:**
- Determines training styles from training examples to characterize the domain and distribution.
- Identifies test styles from the input record to determine its domain and distribution.
- Evaluates the extent to which the test style matches the training styles.
- Determines if the input record belongs to the domain and distribution of the training examples.
- Potential Applications:**
This technology can be used in various fields such as anomaly detection, quality control, and fraud detection where determining the similarity of input data to training examples is crucial.
- Problems Solved:**
This technology addresses the challenge of verifying if new data falls within the domain and distribution of the training data, ensuring the reliability of machine learning model predictions.
- Benefits:**
- Enhances the accuracy and reliability of machine learning models.
- Improves the detection of outliers and anomalies in data.
- Enables better decision-making based on the compatibility of new data with training examples.
- Commercial Applications:**
Potential commercial applications include automated quality control systems, fraud detection software, and predictive maintenance tools for various industries.
- Questions about the Technology:**
1. How does this method improve the performance of machine learning models in real-world applications? 2. What are the key factors that influence the accuracy of determining if new data belongs to the training distribution?
Original Abstract Submitted
A method for detecting whether a given input record of measurement data that is inputted to a trained machine learning model is in the domain and/or distribution of training examples with which the machine learning model was trained. The method includes: determining, from each training example, a training style that characterizes the domain and/or distribution to which the training example belongs; determining, from the given input record of measurement data, a test style that characterizes the domain and/or distribution to which the given record of measurement data belongs; evaluating, based on the training styles and the test style, to which extent the test style is a member of the distribution of the training styles; and based at least in part on the outcome of this evaluation, determining whether the given record of measurement data is in the domain and/or distribution of the training examples.
