Kernel methods combined with large-margin learning algorithms such as SVMs have been used successfully to tackle a variety of learning tasks since their introduction in the early 90s. However, in the standard framework of these methods, the choice of an appropriate kernel is left to the user and a poor selection may lead to sub-optimal performance. Instead, sample points can be used to select a kernel function suitable for the task out of a family of kernels fixed by the user. While this is an appealing idea supported by some recent theoretical guarantees, in experiments, it has proven surprisingly difficult to consistently and significantly outperform simple fixed combination schemes of kernels. This talk will survey different methods and algorithms for learning kernels and will present novel results that tend to suggest that significant performance improvements can be obtained with a large number of kernels. (Includes joint work with Mehryar Mohri and Afshin Rostamizadeh.)