Learning the Kernels for Support Vector Machines
Speaker: Shai Ben-David
Support Vector machines (SVM's) is one of the most useful and widely
applicable machine learning techniques. Each concrete application of SVMs
depends on a successful choice of a "kernel matrix". So far, most of the
work in this area has focused on developing a variety of kernels and
efficient algorithms for employing SVMs with these kernels. Relatively
little research attention has been given to the question of how to pick a
suitable kernel for any particular learning task at hand.
In this work, we analyze exactly that issue. We show that to a certain
extent the problem of choosing a kernel can be dealt with as another
learning task, for a moderate cost in terms of the number of needed
Rather than aiming at the SVM specialists among you, I wish to use the
opportunity to provide a general introduction to the useful ideas of
support vector machines and kernel-based learning. I shall therefore
assume no prior background on these issues, and will not go into the
technical details of the new results.
The new results about learning the kernel are joint work with Nati Srebro
from U of T.