[Ml-stat-talks] Karsten Borgwardt: TIME CHANGE, NOW 2PM
blei at CS.Princeton.EDU
Sun Jul 4 19:42:10 EDT 2010
sorry for the double announcement. karsten's talk is now at 2PM in CS room 402.
*** MACHINE LEARNING SEMINAR ***
Computer Science Building 402
Dr. Karsten Borgwardt,
Machine Learning and Computational Biology Research Group,
Max Planck Institutes Tübingen, Germany
Scalable graph kernels
Kernel methods are a family of algorithms in intelligent data analysis,
which have gained enormous popularity in machine learning over the
last 15 years. One reason for their attractiveness
lies in the fact that the underlying theory of these algorithms can
easily be generalized from
vectorial data to strings, time series, graphs and other types of
In real-world applications, the efficient computation of the kernel
function, i.e. of the similarity measure,
is a key challenge. For strings and time series, efficient computation
techniques for kernels were developed early on,
but graph kernels remained slow to compute and only applicable to
graphs with a few dozen nodes without attributes. A major focus of my
has been to turn graph kernels from a theoretical concept into a
useful tool for practical graph data analysis.
In my talk, I will present my work on efficient graph kernels, in
particular a recent breakthrough from 2009, which now allows for
highly scalable graph kernel computation (Shervashidze, Borgwardt.
Fast Subtree Kernels on Graphs, NIPS 2009).
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