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From Wiki:
Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms
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.
What is the difference?
Multiple kernel learning is restricted to the kernel methods. Ensemble learning can collect together any classification methods -- kernel SVM and Random Forest and logistic regression could all appear in the same ensemble.
You can find a useful explanation on that topic in Sun et a. (2003), particularly in Section 3:
It reaches a conclusion that multiple kernel learning (MKL) is a special instance of ensemble learning (EL). For example, EL does not suffer from the limitation of MKL that the classifiers have the sub-kernels of the same size and the same support vector coefficients (for SVMs).
