[WIP] Matern kernel

Add Matern kernel to the kernels in pairwise.py.

The Matern kernel (https://en.wikipedia.org/wiki/Mat%C3%A9rn_covariance_function and Rasmussen and Williams 2006, pp84) is a generalization of the RBF and the absolute exponential kernel with an additional hyperparemeter nu, which allows interpolating between these two (RBF: nu=inf, absolute exponential: nu=0.5). In contrast to the RBF kernel, it makes less strict assumptions on the smoothness of the function to be learned. This is shown in an example for a step-function for different values of nu:
 
![figure_0](https://cloud.githubusercontent.com/assets/1116263/5189587/64c46cb2-74df-11e4-9b94-684e9751bc78.png)

TODOs:
- [] support for arbitrary values of nu (coef0)
- [] tests
We could reuse the `rbf_kernel` in this case. In this implementation, `euclidean_distances` will compute the squared distances, compute the square root and here we compute the squared distances again which is not as efficient as it could be.