[WIP] Kernel Ridge Regression

Adds an implementation of Kernel Ridge Regression (KRR)

Basic implementation of KRR based on @mblondel's code in lightning. In addition to the lighning code, documentation and an example comparing KRR and SVR is added. The example illustrates the models learned by KRR and SVR:

![plot_kernel_ridge_regression_001](https://cloud.githubusercontent.com/assets/1116263/5330658/9793f8f6-7e03-11e4-93a9-7da2f0634037.png)

Furthermore, it compares the time required for fitting the model and predicting with the model for different sizes of the training set:

![plot_kernel_ridge_regression_002](https://cloud.githubusercontent.com/assets/1116263/5330660/a1b7a4cc-7e03-11e4-8628-3d6cccad921a.png)

I am not sure if this is useful for two reasons: 1) Users may think they need to explicitly use `KernelRidge` to benefit from this speedup in the linear case, but it is actually already taken care of by the `Ridge` object. 2) It is equally acceptable to see the solution of the ridge problem in the `n_samples < n_features` space using what you are referring to as kernelization, just as a smart way of solving the problem by application of the Woodbury matrix identity.