Summer School on Mining Big and Complex Data, Ohrid 2016
The summer school includes lectures on predictive modelling methods for big and complex data. More specifically, the lectures present methods handling the following complexity aspects: (a) structured data as input or output of the prediction process, (b) very large/massive datasets, with many examples and/or many input/output dimensions, where data may be streaming at high rates, (c) incompletely/partially labelled data, and (d) data placed in a spatio-temporal or network context. Each of these is a major challenge to current ML/DM approaches and is the central topic of active research in areas such as structured-output prediction, mining data streams, semi-supervised learning, and mining network data. The applicability and the potential of the presented methods will be demonstrated on several showcases from molecular biology, sensor networks, multimedia, and social networks.
Opening and introduction
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