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#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.
#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.
#Bayesian approaches put priors on the kernel parameters and learn the parameter values from the priors and the base algorithm.
#Boosting approaches add new kernels iteratively until some stopping criteria that is a function of performance is reached.
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