In semi-supervised learning, a small portion of training data is labeled(or categorized) while the rest of the data points are not labeled. The labeled data is used to train the model, and the model then uses this knowledge to analyze and categorize the unlabeled data.
Semi-supervised Learning is considered a hybrid of Supervised and Unsupervised Learning.
Applications:
Semi-supervised learning is often used because labeled data is difficult or expensive to acquire, while unlabeled data is easily available.
types of semi-supervised learning:
Both Self-Training and Co-Training are categorized under weakly supervised learning and feature both supervised and unsupervised learning techniques.
Approaches to Semi-supervised Learning: