[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?
nan does not occur if the kernel's bandwidth gamma is chosen reasonably. Since this method optimizes the bandwidth, nan values might occur for too small bandwidth values in the given range of gamma values.