Distances and kernels have become essential concepts for machine learning applications in computational biology. The talk will focus on their theoretical foundations, clarifies the relationship between the concepts, presents advantages and disadvantages, and gives hints for the derivation of new distance measures and kernels as well as for their application. The focus of the talk will be on distances and kernels for structured data, in particular, sets, sequences, and graphs.


Pour le connaître : http://wwwkramer.in.tum.de/kramer/stefan.html