Semi-Supervised Learning

Semi-supervised learning allows neural networks to mimic human inductive logic and sort unknown information fast and accurately without human intervention. Any problem where you have a large amount of input data but only a few reference points available is a good candidate semi-supervised learning. A classic example is a photo archive with millions of random images. Instead of manually labeling each picture, a human searching for images of people can just tag a few relevant samples from the database. Then the neural network can scour the databank and find every image it believes represents a human.