[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)

In general, we should not expect strictly symmetric positive definite matrices `K`, and the optimization problem to be solved is a least squares problem. So I would have put a least squares solver directly. What is the motivation to try to solve this exactly (using `linalg.solve`)? If it is for speed because of `sym_pos=True` or possibly multi-target issues?
In any case, the `except` part should be solved using a least squares solver instead of calculating the full pseudoinverse (unless of course none of the least squares solvers accepts multiple targets, but I thought there should be at least one that does).