Aplicação de Modelos de Aprendizado Semissupervisionado na Classificação de Imagens de Sensoriamento Remoto

In many applications, the dearth of information to a proper training and use of supervised Machine Learning methods is a persistent problem. This fact led to the development of the semi-supervised learning paradigm. This paradigm can be understood as a combination of concepts of unsupervised and supervised paradigms. The way how the learning is conducted allows to organize the semi-supervised methods in different models. This paper presents a comparative study between different semisupervised learning models. It is also proposed a semi-supervised version of SVM, which achieved better performance in the comparisons made.