Robert bosch gmbh (20240303482). PRIORITIZING TRAINING EXAMPLES WHEN TRAINING CLASSIFIERS simplified abstract
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 20240303482 titled 'PRIORITIZING TRAINING EXAMPLES WHEN TRAINING CLASSIFIERS
- Simplified Explanation:**
This patent application describes a method for prioritizing training examples in a data set for a classifier that maps measurement data to classification scores. The method involves training the classifier with the examples, generating modifications for at least one example, determining classification scores from the modifications, and assigning a priority to the example based on the distribution of these scores.
- Key Features and Innovation:**
- Prioritizing training examples in a data set for a classifier
- Mapping measurement data to classification scores
- Generating modifications for training examples
- Determining classification scores using the classifier
- Assigning priority based on the distribution of scores
- Potential Applications:**
This technology could be applied in various fields such as:
- Machine learning
- Data analysis
- Pattern recognition
- Problems Solved:**
- Efficient prioritization of training examples
- Improved accuracy in classification tasks
- Enhanced performance of classifiers
- Benefits:**
- Increased efficiency in training classifiers
- Enhanced accuracy in classification tasks
- Improved performance in mapping measurement data to classification scores
- Commercial Applications:**
- Title: "Enhanced Training Example Prioritization Technology"
- This technology could be utilized in industries such as:
- Healthcare for disease diagnosis - Finance for fraud detection - Marketing for customer segmentation
- Questions about the Technology:**
1. How does this method improve the efficiency of training classifiers? 2. What are the potential implications of using this technology in data analysis tasks?
- Frequently Updated Research:**
Stay updated on advancements in machine learning algorithms and data analysis techniques to enhance the application of 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.
