LVQ is a supervised machine learning algorithm that uses prototype-based classification. It maps input data to discrete class labels by adjusting vectors that represent classes.
It operates by:
- Assigning a set of prototype vectors to each class
- Updating the prototypes to better match input vectors during training
Compared to Generalized Regression Neural Network and Levenberg-Marquardt, LVQ may be less effective in continuous regression problems but can be useful for classification tasks.