In this example we show how to perform Multiple Kernel Learning (MKL)
with the modular interface for multi-class classification.
First, we create a number of base kernels and features.
These kernels can capture different views of the same features, or actually
consider entirely different features associated with the same example
(e.g. DNA sequences = strings AND gene expression data = real values of the same tissue sample).
The base kernels are then subsequently added to a CombinedKernel, which
contains a weight for each kernel and encapsulates the base kernels
from the training procedure. When the CombinedKernel between two examples is
evaluated it computes the corresponding linear combination of kernels according to their weights.
We then show how to create an MKLMultiClass classifier that trains an SVM and learns the optimal
weighting of kernels (w.r.t. a given norm q) at the same time. The main difference to the binary
classification version of MKL is that we can use more than two values as labels, when training
the classifier.
Finally, the example shows how to classify with a trained MKLMultiClass classifier.

