17809463. COMPUTER TRAINING DATA USING MACHINE LEARNING simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
COMPUTER TRAINING DATA USING MACHINE LEARNING
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
COMPUTER TRAINING DATA USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17809463 titled 'COMPUTER TRAINING DATA USING MACHINE LEARNING
Simplified Explanation
The abstract describes a method for training data models using machine learning. Here is a simplified explanation of the abstract:
- The method involves training a computer data model using a training data set.
- The training data set includes both training data and additional training data.
- The data distribution of the training data set is represented by layers of data.
- The computer data model is iteratively trained for each layer of the training data set.
- Statistical noise is randomly added to each layer of the training data set.
- Data variations are detected in each layer of the additional training data.
- The data variations are diluted in each additional layer of the training data.
- The computer data model is retrained using the diluted data variations in each layer of the additional training data.
Potential applications of this technology:
- Improving the accuracy and performance of machine learning models.
- Enhancing data analysis and prediction capabilities in various industries.
- Optimizing decision-making processes based on large datasets.
Problems solved by this technology:
- Overfitting: By adding statistical noise and diluting data variations, the method helps prevent overfitting, where a model becomes too specialized to the training data and performs poorly on new data.
- Data distribution representation: The use of layers to represent the data distribution allows for a more comprehensive understanding of the training data set.
Benefits of this technology:
- Improved model accuracy: By iteratively training the model for each layer and detecting data variations, the method helps create a more accurate and robust data model.
- Generalization capability: Diluting data variations helps the model generalize better to new data, improving its performance in real-world scenarios.
- Efficient training process: The iterative training approach allows for efficient utilization of the training data set, leading to faster and more effective model training.
Original Abstract Submitted
Training data models using machine learning can include training a computer data model of data distribution using a training data set. The training data set includes training data and additional training data, and the training data and the additional training data being represented by layers of data representing the data distribution of the training data set. The computer data model using the additional training data is iteratively trained for each of the layers of the training data set. Statistical noise is added randomly to each of the layers of the training data set. Data variations are detected in each of the layers of the additional training data. The data variations are diluted in each of the additional layers of the training data, and the computer data model is retrained for the training data set using the diluted data variations in each of the layers of the additional training data.
