Semi-supervised Learning

• Semi-supervised learning is a type of machine learning that uses a combination of labeled and unlabeled data for training.
• The reason for using unlabeled data is because its easier to obtain and less expensive than labeled data.
• Semi-supervised learning algorithms are useful when there is a small amount of labeled data and a large amount of unlabeled data.
• The objective of semi-supervised learning is to improve the performance of supervised learning algorithms by using unlabeled data.

Info

Most semi-supervised learning algorithms are a combination of supervised and unsupervised learning algorithms.