  With the advent of kernel methods, automating the task of specifying a
suitable kernel has become increasingly important. In this context, the
Multiple Kernel Learning (MKL) problem of finding a combination of
pre-specified base kernels that is suitable for the task at hand has received
significant attention from researchers. In this paper we show that Multiple
Kernel Learning can be framed as a standard binary classification problem with
additional constraints that ensure the positive definiteness of the learned
kernel. Framing MKL in this way has the distinct advantage that it makes it
easy to leverage the extensive research in binary classification to develop
better performing and more scalable MKL algorithms that are conceptually
simpler, and, arguably, more accessible to practitioners. Experiments on nine
data sets from different domains show that, despite its simplicity, the
proposed technique compares favorably with current leading MKL approaches.
