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where <math>\Epsilon</math> is an error function and <math>R</math> is a regularization term. <math>\Epsilon</math> is typically the square loss function (Tikhonov regularization) or the hinge loss function (for [[Support vector machine|SVM]] algorithms), and <math>R</math> is usually an <math>\ell_n</math> norm or some combination of the norms (i.e. [[elastic net regularization]]).
===Supervised learning===
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=review>http://www.jmlr.org/papers/volume12/gonen11a/gonen11a.pdf</ref>
#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.
For more information on these methods, see Gonen and Alpaydın (2011) <ref name=review>http://www.jmlr.org/papers/volume12/gonen11a/gonen11a.pdf</ref>
===Semisupervised learning===
[[Semisupervised learning]] approaches to multiple kernel learning are similar to other extensions of
===Unsupervised learning===
Unsupervised multiple kernel learning algorithms have also been advanced. These algorithms use
==MKL Libraries==
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