## Learning Graphical Models with Mercer Kernels

Francis R. Bach

(Professor Michael I. Jordan)

Intel Corporation, (NSF) IIS-9988642, and (ONR/MURI) N00014-00-1-0637

We present a class of algorithms for learning the structure of
graphical models from data. The algorithms are based on a
measure known as the kernel generalized variance (KGV),
which essentially allows us to treat all variables on equal footing
as Gaussians in a feature space obtained from Mercer kernels. Thus we
are able to learn hybrid graphs involving discrete and continuous variables
of arbitrary type. We explore the computational properties of
our approach, showing how to use the kernel trick to compute
the relevant statistics in linear time. We illustrate our framework
with experiments involving discrete and continuous data.

- [1]
- F. R. Bach and M. I. Jordan, "Learning Graphical Models with Mercer Kernels,"
*NIPS* (to appear).

More information (http://www.cs.berkeley.edu/~fbach) or

Send mail to the author : (fbach@cs.berkeley.edu)

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