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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>
 
1. #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.
2.
 
#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==