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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 while allowing for more automated machine learning methods, 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. Instead of creating a new kernel, multiple kernel algorithms can be used to combine kernels already established for each individual data source.

Multiple kernel learning approaches have been used in many applications, such as event recognition in video,^[1] object recognition in images,^[2] and biomedical data fusion.^[3]

^ Lin Chen, Lixin Duan, and Dong Xu, "Event Recognition in Videos by Learning From Heterogeneous Web Sources," in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2666-2673
^ Serhat S. Bucak, Rong Jin, and Anil K. Jain, Multiple Kernel Learning for Visual Object Recognition: A Review. T-PAMI, 2013.
^ Yu et al. L2-norm multiple kernel learning and its application to biomedical data fusion. BMC Bioinformatics 2010, 11:309