Multiple kernel learning

Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels, reducing bias due to kernel selection, and b) combining data from different sources (e.g. sound and images from a video) that have different notions of similarity and thus require different kernels. This is especially true when each data source already has established kernels that can be used.