Semi-supervised learning is a class of machine learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.
Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data).
Many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy over unsupervised learning (where no data is labeled), but without the time and costs needed for supervised learning (where all data is labeled).
See also:
- Chapelle, Schölkopf, Zien - «Semi-Supervised Learning» (2006)
- What is «unsupervised learning»?
- What is «supervised learning»?
- What is «reinforcement learning»?
- What is the difference between unsupervised and reinforcement learning?
- What is the difference between supervised and reinforcement learning?