Co-Training [Semi-Sup]

Description

Co-training is another semi-supervised learning technique that trains multiple classifiers on different views of the data. A view is a subset of features that are sufficient for the learning task and are conditionally independent given the class label. The basic idea is to use one classifier's predictions to label some of the unlabeled data, and then use that newly labeled data to train the other classifier. This process is performed iteratively, with each classifier improving the other until a stopping criterion is met.

Example

To apply semi-supervised learning in a specific domain, let's consider a medical domain where we want to classify scientific articles into different categories such as cardiology, neurology, and oncology. Suppose we have a small set of labeled articles and a large set of unlabeled articles.

We can use co-training by splitting the features into two views, such as the abstract and the full text of the articles. We would train two classifiers, one for each view, and iteratively update the classifiers using the predictions made by the other classifier on the unlabeled data.