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A novel approach to active sampling is proposed for the semi automatic selection of training patterns in a given pool of candidates. In the method proposed, each candidate is ranked with a double criterion: first, informativeness of the candidate is assessed using the Support Vector Machine (SVM) real valued decision function. Then, diverse sampling is ensured by considering the relative position...
In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the...
In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated...
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