View source for 17891635. Leveraging Machine Learning Models to Identify Missing or Incorrect Labels in Training or Testing Data simplified abstract (GOOGLE LLC)

=Leveraging Machine Learning Models to Identify Missing or Incorrect Labels in Training or Testing Data=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:James Bradley Wendt of San Francisco CA (US)|James Bradley Wendt of San Francisco CA (US)]][[Category:James Bradley Wendt of San Francisco CA (US)]]

[[:Category:Sandeep Tata of San Francisco CA (US)|Sandeep Tata of San Francisco CA (US)]][[Category:Sandeep Tata of San Francisco CA (US)]]

[[:Category:Lauro Ivo Beltrao Colaco Costa of Kirkland WA (US)|Lauro Ivo Beltrao Colaco Costa of Kirkland WA (US)]][[Category:Lauro Ivo Beltrao Colaco Costa of Kirkland WA (US)]]

[[:Category:Emmanouil Koukoumidis of Kirkland WA (US)|Emmanouil Koukoumidis of Kirkland WA (US)]][[Category:Emmanouil Koukoumidis of Kirkland WA (US)]]

==Leveraging Machine Learning Models to Identify Missing or Incorrect Labels in Training or Testing Data - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17891635 titled 'Leveraging Machine Learning Models to Identify Missing or Incorrect Labels in Training or Testing Data

==Simplified Explanation==

**Explanation:**
- Labels are often over labeled by machine-learning models and under labeled by human labelers.
- Solution involves both machine-learning model and human labeling a document, then sending it to a parser to determine discrepancies.
- Discrepancies are presented to a human for review and decision on identified labels.
- Feedback is given to machine-learning model for improvement in confidence calculations.
- Confidence threshold determines if identified labels are presented.

**Potential Applications:**
- Document classification
- Sentiment analysis
- Image recognition

**Problems Solved:**
- Over labeling by machine-learning models
- Under labeling by human labelers
- Inconsistencies in labeling accuracy

**Benefits:**
- Improved accuracy in labeling
- Efficient use of machine-learning models and human input
- Enhanced performance of document parsing and classification systems


==Original Abstract Submitted==

Labels are often over labeled by machine-learning models and under labeled by human labelers. A solution to the over and under labeling problem is to have both a machine-learning model and a human label a document, then send the document to a parser to determine the discrepancies. The discrepancies are then presented to a human to review and decide whether the machine-learning model identified labels are labels. The feedback is then given to the machine-learning model for further improvement in its confidence calculations which via a confidence threshold determine if the identified labels are presented.



[[Category:G06N20/00]]