Kernel-based Machine Learning from Multiple Information Sources: Learning Theory, Algorithms, and Applications in Visual Image Recognition and Computational Biology

Prof. Marius Kloft
Germany, Humboldt University of Berlin, Department of Computer Science
In my talk I will introduce multiple kernel learning, a machine learning framework for integrating multiple types of representation into the learning process. The talk will focus on strategies for effective regularization and flexible kernel combination, including localization. The applicability of the methodology is illustrated by applications taken from the domains of visual object recognition and computational biology.