Google llc (20240119295). Generalized Bags for Learning from Label Proportions simplified abstract
Contents
- 1 Generalized Bags for Learning from Label Proportions
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Generalized Bags for Learning from Label Proportions - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Generalized Bags for Learning from Label Proportions
Organization Name
Inventor(s)
Rishi Saket of Bangalore, Karnataka (IN)
Aravindan Raghuveer of Bangalore, Karnataka (IN)
Balaraman Ravindran of Chennai, Tamil Nadu (IN)
Generalized Bags for Learning from Label Proportions - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119295 titled 'Generalized Bags for Learning from Label Proportions
Simplified Explanation
The present disclosure relates to a method involving data bags, training bags, and machine-learned prediction models.
- Data bags contain instances and proportion labels.
- Training bags are generated from data bags based on weights.
- Bag-level predicted proportion label error correlates to instance-level predicted proportion label error.
Potential Applications
This technology could be applied in various fields such as data analysis, machine learning, and predictive modeling.
Problems Solved
This technology helps in improving the accuracy of machine-learned prediction models by correlating bag-level and instance-level predicted errors.
Benefits
The method enhances the performance of prediction models by optimizing the training data generation process.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of advanced predictive analytics software for industries like finance, healthcare, and marketing.
Possible Prior Art
Prior art may include similar methods for optimizing training data in machine learning models, but the specific correlation between bag-level and instance-level predicted errors may be a novel aspect of this technology.
Unanswered Questions
How does this method compare to traditional data bag generation techniques in terms of accuracy and efficiency?
This article does not provide a direct comparison between this method and traditional data bag generation techniques. Further research or experimentation may be needed to determine the comparative advantages of this technology.
What are the potential limitations or challenges in implementing this method in real-world applications?
The article does not address potential limitations or challenges in implementing this method. Factors such as scalability, computational resources, and data complexity could pose challenges that need to be explored further.
Original Abstract Submitted
example aspects of the present disclosure relate to an example method. the example method includes obtaining, by a computing system comprising one or more processors, a plurality of data bags. in the example method, each respective data bag of the plurality of data bags comprises a respective plurality of instances and is respectively associated with one or more proportion labels. the example method also includes generating, by the computing system, a plurality of training bags from the plurality of data bags according to a plurality of weights. in the example method, the training bags are generated such that a bag-level predicted proportion label error by a machine-learned prediction model over the plurality of training bags correlates to an instance-level predicted proportion label error by the machine-learned prediction model.
