The term Semi-Supervised Learning originates from the fields of Artificial Intelligence, Big Data and Smart Data, as well as Digital Transformation. Semi-Supervised Learning means „partially supervised learning“ in German and is a method by which computers can learn independently from data.
Unlike conventional „Supervised Learning“, where all information (for example, thousands of photos of cats and dogs, all clearly labelled) is annotated by humans, Semi-Supervised Learning works with only a small amount of labelled data and a large amount of unlabelled data. This saves a lot of time and costs, as not every single image or piece of information needs to be reviewed by a human.
A simple example: Imagine a company wants to automatically sort its emails into „spam“ and „not spam“. It only has 100 emails already marked as „spam“ or „not spam“, but thousands are still unmarked. Semi-supervised learning helps to analyse these unmarked emails and improve the categorisation by recognising patterns.
This makes artificial intelligence more efficient and capable of making valuable decisions more quickly, even when little prepared data is available.
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