Title: Predictive Analytics for Dependent Data
Supervisor: Luis Torgo; Co-supervisor: Vitor S. Costa, University of Porto, Portugal
External Advisor: Annalisa Appice, University of Bari, Italy
MAP-i Doctoral Programme in Computer Science
Summary
The main goal of this thesis consists in studying modelling approaches that can handle different types of data dependencies. This involves studying, developing and evaluating methodologies that are able to cope with different classes of contextual dependencies in such a way that this information is taken into account when obtaining predictive models. Different types of data dependencies will be considered in this study, ranging from simple temporal dependencies to more complex contextual information (e.g. data collected in arbitrary networks).
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