[WIP] Kernel regression

Implementation of Nadaraya-Watson kernel regression with automatic bandwidth selection.

This simple algorithm is suited as a baseline for more complex algorithms, particularly in cases where the size of the training set is large compared to number of dimensions. An advantage of this algorithm is that it allows efficient bandwidth selection via leave-one-out cross-validation. This PR includes an example comparing kernel regression with SVR on a simple 1D toy dataset:

![figure_1](https://cloud.githubusercontent.com/assets/1116263/4668304/37ff234c-5564-11e4-99f9-9e526e1cd2c5.png)

![figure_2](https://cloud.githubusercontent.com/assets/1116263/4668310/401355a8-5564-11e4-85cd-410788904d08.png)

Potential future extensions of this could include metric learning for kernel regression (http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.117.7080)

Any opinions on whether this is interesting for sklearn?
Are `nan` results regular when the kernel is sensibly chosen?