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For supervised learning, there are many other algorithms that use different methods to learn the form of the kernel. The following categorization has been proposed by Gonen and Alpaydın (2011) <ref name="supervised_review">Mehmet Gönen, Ethem Alpaydın. [http://www.jmlr.org/papers/volume12/gonen11a/gonen11a.pdf Multiple Kernel Learning Algorithms] Jour. Mach. Learn. Res. 12(Jul):2211−2268, 2011</ref>
#Fixed rules approaches, such as the linear combination algorithm described above. These do not require parameterization and use rules like summation and multiplication to combine the kernels. The weighting is learned in the algorithm. Other examples of fixed rules include pairwise kernels, which are of the form <math>k((x_{1i}, x_{1j}),(x_{2i},x_{2j}))=k((x_{1i},x_{2i}),(x_{1j}),x_{2j}))</math>
#Heuristic approaches. These algorithms use a combination function that is parameterized. The parameters are generally defined for each individual kernel based on single-kernel performance or some computation from the kernel matrix.
#Optimization approaches. These approaches solve an optimization problem to determine parameters for the kernel combination function.
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