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Titlebook: Algorithms and Architectures for Parallel Processing; 18th International C Jaideep Vaidya,Jin Li Conference proceedings 2018 Springer Natur

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,Die heutigen wirtschaftlichen Verhältnisse, proposed so far, most of them work for these data that have all classes of tagged data. A few methods work for positive and unlabeled data and the computational complexity of existing methods is particularly high and they can’t work well for big data. In this paper, we proposed an active learning a
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Algorithms and Architectures for Parallel Processing18th International C
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https://doi.org/10.1007/978-3-658-35783-2ive to the number of the intermediate results. For the purpose of intermediate results reduction, . is proposed for nearest neighbor query based on pairwise similarity. Since only first top-. neighbors are output for each document, the size of records shuffled is kept in the same magnitude as input
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