18247348. LEARNING METHOD, ESTIMATION METHOD, LEARNING APPARATUS, ESTIMATION APPARATUS, AND PROGRAM simplified abstract (Nippon Telegraph and Telephone Corporation)
Contents
LEARNING METHOD, ESTIMATION METHOD, LEARNING APPARATUS, ESTIMATION APPARATUS, AND PROGRAM
Organization Name
Nippon Telegraph and Telephone Corporation
Inventor(s)
LEARNING METHOD, ESTIMATION METHOD, LEARNING APPARATUS, ESTIMATION APPARATUS, AND PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18247348 titled 'LEARNING METHOD, ESTIMATION METHOD, LEARNING APPARATUS, ESTIMATION APPARATUS, AND PROGRAM
Simplified Explanation
The abstract describes a learning method that allows a computer to estimate a parameter of a topic model using a smaller amount of data than what is typically required. This method involves inputting multiple data sets and using them to learn an estimation model.
- The learning method allows a computer to estimate a parameter of a topic model.
- The method is designed to work with a smaller amount of data than what is normally required.
- Multiple data sets are inputted into the learning process.
- An estimation model is learned based on the input data sets.
Potential Applications
- Natural language processing: This learning method can be applied to analyze large amounts of text data and extract meaningful topics.
- Data analysis: The estimation model can be used to uncover hidden patterns and insights in various types of data sets.
- Recommendation systems: By understanding the topics within a dataset, the method can help improve the accuracy of recommendation algorithms.
Problems Solved
- Limited data availability: The learning method allows for the estimation of topic model parameters even when only a smaller amount of data is available.
- Computational efficiency: By using a smaller amount of data, the learning process can be faster and more efficient.
- Resource constraints: This method can be beneficial in situations where there are limitations on computational resources or storage capacity.
Benefits
- Improved efficiency: The learning method enables the estimation of topic model parameters using less data, saving time and computational resources.
- Enhanced accuracy: By learning from multiple data sets, the estimation model can provide more accurate parameter estimates for topic modeling.
- Flexibility: The method can be applied to various domains and datasets, making it adaptable to different industries and research fields.
Original Abstract Submitted
A learning method according to an embodiment causes a computer to execute: an input step of inputting a plurality of data sets; and a learning step of learning, based on the plurality of input data sets, an estimation model for estimating a parameter of a topic model from a smaller amount of data than an amount ot data included in the plurality of data sets.
