18182215. PRIORITIZING TRAINING EXAMPLES WHEN TRAINING CLASSIFIERS simplified abstract (Robert Bosch GmbH)
Contents
PRIORITIZING TRAINING EXAMPLES WHEN TRAINING CLASSIFIERS
Organization Name
Inventor(s)
Laura Beggel of Stuttgart (DE)
William Harris Beluch of Stuttgart (DE)
PRIORITIZING TRAINING EXAMPLES WHEN TRAINING CLASSIFIERS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18182215 titled 'PRIORITIZING TRAINING EXAMPLES WHEN TRAINING CLASSIFIERS
The abstract describes a method for prioritizing training examples in a data set for a classifier that maps measurement data to classification scores for predetermined classes.
- Simplified Explanation:
- The method involves training a classifier with examples from the data set, generating modifications for at least one example, determining classification scores from the modifications, and prioritizing examples based on these scores.
- Key Features and Innovation:
- Training a classifier to map measurement data to classification scores. - Generating modifications for training examples. - Determining classification scores from modifications. - Prioritizing training examples based on the distribution of classification scores.
- Potential Applications:
- Machine learning systems. - Data analysis tools. - Classification algorithms.
- Problems Solved:
- Efficient prioritization of training examples. - Improved accuracy in classification tasks. - Enhanced performance of classifiers.
- Benefits:
- Increased efficiency in training data selection. - Enhanced accuracy in classification tasks. - Improved performance of machine learning models.
- Commercial Applications:
- This technology can be used in various industries such as healthcare, finance, and marketing for data analysis and classification tasks.
- Questions about the Technology:
1. How does this method improve the accuracy of classification tasks? 2. What are the potential applications of this technology in real-world scenarios?
- Frequently Updated Research:
- Stay updated on advancements in machine learning algorithms and data analysis techniques related to this technology.
Original Abstract Submitted
A method for prioritizing training examples in a training data set for a classifier designed to map measurement data to classification scores with respect to classes of a predetermined classification. In the method includes: the classifier is trained with the training examples from the training data set; modifications are generated for at least one training example; classification scores are respectively determined from the modifications by means of the classifier; a priority of the training example to which the modifications belong is determined from the distribution of these classification scores.
