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Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as...
Semi-Supervised Learning (SSL) is a learning paradigm in which the classification task is performed by taking into account just a few labeled instances. The unlabeled instances also participate in the process, but by providing additional information about the dataset. In this paper, a new semi-supervised technique based on interacting forces is proposed. Both labeled and unlabeled instances play different...
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