Kernel methods have found applications throughout much of machine
learning and have been successful in a variety of tasks.  The proper
selection of a kernel function plays a crucial role in the performance
of kernel-based algorithms.  Despite the popularity and success of
kernel-based algorithms, until recently, there has not been much focus
on how to select a kernel function in a principled fashion. In this
talk I will present an overview of automatic kernel selection
and introduce our most recent results.