Antônio José de Lima Batista

Abstract

There are many situations in machine learning in which it is easy to obtain vast amounts of unlabeled data, but it is costly to obtain labels for these data. This leads to typical situations in which only a small subset of labeled data is available, which is not representative enough for learning a model in a supervised way. In such situations, techniques that are able to learn from both …