proxylabeling
Proxylabeling is a technique used in machine learning and artificial intelligence to assign labels to data points based on their proximity to other labeled data points. The core idea is that if two data points are similar or close to each other in a feature space, they are likely to share the same label. This is particularly useful in scenarios where obtaining labels for all data is expensive or time-consuming.
The process typically involves an initial set of labeled data, often called seeds. Then, an algorithm analyzes
Proxylabeling is often integrated into semi-supervised learning frameworks. It can be used to bootstrap the learning