Semi-supervised learning

Semi-supervised learning is a type of learning that sits in between supervised and unsupervised learning, where you have got training examples with input variables (X), but only some of them are labeled/tagged with the output variable (Y).

A good example of this type of learning is Flickr (https://www.flickr.com/), where you have got lots of images uploaded by users but only some of them are labeled (such as sunset, ocean, and dog) and the rest are unlabeled.

To solve the tasks that fall into this type of learning, you can use one of the following or a combination of them:

• Supervised learning: Learn/train the learning algorithm to give predictions about the unlabeled data and then feed the entire training samples back to learn from it and predict the unseen data
• Unsupervised learning: Use the unsupervised learning algorithms to learn the underlying structure of the explanatory features or the input variables as if you don't have any tagged training samples