Machine Learning

Our Laboratory is interested in the study and development of machine learning techniques mainly for classification, feature learning, active learning, temporal series analysis, feature selection, and clustering purposes. Thus, our research very often includes development and adaptation of supervised and unsupervised learning approaches, typically based on Neural Networks, Evolutionary Algorithms, and Support Vector Machines. We also work on strategies to improve the effectiveness of classification systems by more effective ensemble of classifiers according to applications needs. Finally, we are very interested on the development of new active learning strategies aiming at the inclusion of the user in the training process.

Some relevant work in this topic are:

K. Nogueira, O. A. B. Penatti, J. A. dos Santos. Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, 539-556, 2017.
O. A.. Penatti, K. Nogueira, J. A. dos Santos, Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 44-51), 2015.
F. A. Faria, J. A. dos Santos, A. R. Rocha, R. da S. Torres. A Framework for Selection and Fusion of Pattern Classifiers in Multimedia Recognition. Pattern Recognition Letters, v. 39, p. 52-64, 2014.