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Supervised classification techniques are known to exploit physical information of the analysed data, such as similarity, distribution and other low level features. Despite the relevance of such features, recent works have showed that a higher variety of patterns can be detected by combining low level and high level features. In this paper, it is proposed a supervised classification technique which...
We propose a new supervised classification technique which considers the ease of access of unlabeled instances to training classes through an underlying network. The training data set is used to construct a network, in which instances (nodes) represent the states that a random walker visits, and the network link structure is modified by performing a link weight composition between the unlabeled instance...
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