Frederico Damasceno Bortoloti

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In many real-world tasks a lot of unlabeled data are collected over time and, although they may be useful to improve the quality of classification models, they are usually ignored. Semi-supervised learning techniques combine unlabeled and labeled data to capture more useful information about a particular task. On the other hand, an incremental learning(More)
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