Datenschutzerklärung|Data Privacy

A. Borusan

28.11.2016, 14 Uhr s.t. DFKI Projektbüro Berlin, 4th Floor, Room: Weizenbaum, Alt-Moabit 91 C, 10559 Berlin: "Beyond Independence: Efficient Learning Techniques for Networks and Temporal Data" (Prof. Dr. Stephan Günnemann Department of Informatics Techn

Going beyond independence, most of the data gathered in today's applications show complex dependency structures: people, for example, interact with each other in social networks; similarly, sensors in a cyber-physical system continuously measure dependent signals over time. In general, networks and temporal data are the most frequently observed examples for such complex data. In this talk, I will focus on two data mining tasks that operate in these domains: (i) Classification in (partially) labeled networks, and (ii) anomaly detection for temporal rating data. For both tasks I will present the underlying modeling principles, I will sketch solutions how to derive efficient learning algorithms, and I will showcase their applications in different scenarios. The talk concludes with a summary of further research our group is working on.