What is semi-supervised Learning?

Semi-supervised learning is a technique in between supervised and unsupervised learning. Arguably, it should not be a category of machine learning but only a generalization of supervised learning, but it’s useful to introduce the concept separately. Its aim is to reduce the cost of gathering labelled data by extending a few labels to similar unlabeled data. Some generative models are classified semi-supervised approaches. Semi-supervised learning can be divided into transudative and inductive learning. Transudative learning is when we want to infer the labels for unlabeled data. The goal of inductive learning is to infer the correct mapping from inputs to outputs. We can see this process as similar to most of the learning we had at school. The teacher shows the students a few examples and gives them some to take home; to solve those, they need to generalize.

In semi-supervised learning, the model is trained on a combination of labeled and unlabeled data. The labeled data provides the model with information about what to expect in each input, while the unlabeled data helps it generalize from one input to another, AI is also not as good at dealing with completely new problems such as predicting the outcome of a natural disaster that has never happened before. This means it can’t replace human analysts, but can still help them do their jobs by automating simple tasks and providing analysis on current data that humans could be spending time on. The online workshop: “What the Digital Revolution Means for Business unemployment and the acceleration of inequality.

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