Titlebook: Discovery Science; 18th International C Nathalie Japkowicz,Stan Matwin Conference proceedings 2015 Springer International Publishing Switze

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Semi-supervised Learning for Stream Recommender Systems,them. Only a small fraction of items can be rated by a single user. Consequently, there is plenty of unlabelled information that can be leveraged by semi-supervised methods. We propose the first semi-supervised framework for stream recommender systems that can leverage this information incrementally

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Multi-label Classification via Multi-target Regression on Data Streams,, however, in the streaming setting, comparatively few methods exist. In this paper, we propose a new methodology for multi-label classification via multi-target regression in a streaming setting and develop a streaming multi-target regressor iSOUP-Tree, which uses this approach. We experimentally e

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Periodical Skeletonization for Partially Periodic Pattern Mining,partially periodic patterns, where typical periods (e.g., daily or weekly) can be considered. Although efficient algorithms have been studied, applying them to real databases is still challenging because they are noisy and most transactions are not extremely frequent in practice. They cause a combin

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BUMP

Dr. Inventor Framework: Extracting Structured Information from Scientific Publications,nsiderable part of on-line scientific literature is still available in layout-oriented data formats, like PDF, lacking any explicit structural or semantic information. As a consequence the bootstrap of textual analysis of scientific papers is often a time-consuming activity. We present the first ver

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