Solved – the difference between multiple kernel learning and ensemble learning

Question

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?

Answer 1

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.