  Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting to
learn not only a classifier/regressor but also the best kernel for the training
task, usually from a combination of existing kernel functions. Most MKL methods
seek the combined kernel that performs best over every training example,
sacrificing performance in some areas to seek a global optimum. Localized
kernel learning (LKL) overcomes this limitation by allowing the training
algorithm to match a component kernel to the examples that can exploit it best.
Several approaches to the localized kernel learning problem have been explored
in the last several years. We unify many of these approaches under one simple
system and design a new algorithm with improved performance. We also develop
enhanced versions of existing algorithms, with an eye on scalability and
performance.
