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Supervised learning uses a training set of labeled examples to compute a classifier which is a mapping from feature vectors to class labels. The success of a learning algorithm is evaluated by its ability to generalize, i.e., to extend this mapping accurately to new data that is commonly referred to as the test data. Good generalization depends crucially on the quality of the training set. Because...
Classification aims to define an abstract model of a set of classes, called classifier, which is built from a set of labeled data, the training set. However, in large or correlated data sets, association rule mining may yield huge rule sets. Hence several pruning techniques have been proposed to select a small subset of high-quality rules. Since the availability of a ldquorichrdquo rule set may improve...
The available cases with actual classes are not enough for building telecom clientspsila credit classification model in practice, especially for the newly established system in which old customerspsila data do not exist. For evaluating telecom clientspsila credit, a classifier based on active learning is proposed in this paper. Active learning aims at reducing the number of training examples to be...
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