Learning with Dependent Data: With Applications in Computational Genome Analysis

• Applicant: Professor Dr. Marius Kloft
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing ; Theoretical Computer Science
• Term: from 2012 to 2014
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 225910935

The classical machine learning theory is built upon the assumption of independent and identically distributed random variables. In practical applications, however, this assumption is often violated, for instance, when the data exhibits temporal or spatial correlations. In the proposed program, we will contribute to the theory of learning with dependent data, the development of efficient and effective learning machines, and the application thereof to computational genome analysis.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Dr. Mehryar Mohri, Ph.D.
• Participating Person: Professor Dr. Gunnar Rätsch