Semi-Supervised Learning

Semi-supervised learning is a type of machine learning.

It refers to a learning problem (and algorithms designed for the learning problem) that involves a small portion of labeled examples and a large number of unlabeled examples from which a model must learn and make predictions on new examples. As such, it is a learning problem that sits between supervised learning and unsupervised learning. We require semi-supervised learning algorithms when working with data where labeling examples is challenging or expensive. The sign of an effective semi-supervised learning algorithm is that it can achieve better performance than a supervised learning algorithm fit only on the labeled training examples.

Semi-supervised learning algorithms generally are able to clear this low bar expectation. Finally, semi-supervised learning may be used or may contrast inductive and transductive learning.

Generally, inductive learning refers to a learning algorithm that learns from labeled training data and generalizes to new data, such as a test dataset. Transductive learning refers to learning from labeled training data and generalizing to available unlabeled (training) data. Both types of learning tasks may be performed by a semi-supervised learning algorithm.

Hi Swapneel. This topic seems very interesting.

Could you give more details and probably some examples of how this is done. Maybe you can illustrate more on the inductive learning or the transductive learning, and how traditional algorithms would be applicable in this case. Also myabe if you have any sample dataset, and a piece of code working on top of that, it’ll be more clear.

This is definitely the challenge in current problems I’m facing and it seems semi-supervised learning could help.