Machine Learning Algorithms - Second Edition

Semi-supervised learning

There are many problems where the amount of labeled samples is very small compared with the potential number of elements. A direct supervised approach is infeasible because the data used to train the model couldn't be representative of the whole distribution, so therefore it's necessary to find a trade-off between a supervised and an unsupervised strategy. Semi-supervised learning has been mainly studied in order to solve these kinds of problems. The topic is a little bit more advanced and won't be covered in this book (the reader who is interested can check out Mastering Machine Learning Algorithms, Bonaccorso G., Packt Publishing). However, the main goals that a semi-supervised learning approach pursues are as follows: ...