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Decision trees have been widely used for online learning classification. Many approaches usually need large data stream to finish decision trees induction, as show notable limitations (even fail) with small data stream. In fact, there exist many real instances with small data stream. In the paper, we propose a novel incremental extremely random forest algorithm, dealing with online learning classification...
In this paper, we present a local-driven semi-supervised learning framework to propagate the labels of the training data (with multi-label) to the unlabeled data. Instead of using each datum as a vertex of graph, we encode each extracted local feature descriptor as a vertex, and then the labels for each vertex from the training data are derived based on the context among different training data, finally...
Relevance feedback has been developed for several years and becomes an effective method for capturing userpsilas concepts to improve the performance of content-based image retrieval (CBIR). In contrast to fully labeled training dataset in supervised learning, semi-supervised learning and active learning deal with training dataset with only a small portion of labeled samples. This is more realistic...
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