Error-Driven Learning
A machine learning algorithm that adjusts the weights of a neural network based on the errors between its predicted output and the actual output.
Areas of application
- Natural Language Processing
- Speech Recognition
- Recommendation Systems
- Robotics and Control
- Medical Diagnosis and Treatment
Example
For instance, an image classification model can use error-driven learning to update its weights based on the difference between its predicted class labels and the actual class labels of the images in the training dataset.