[MRG] GSoC 2014: Standard Extreme Learning Machines

Finished implementing the standard extreme learning machines (ELMs). I am getting the following results with 550 hidden neurons against the digits datasets,

Training accuracy using the logistic activation function: 0.999444
Training accuracy using the tanh activation function: 1.000000

Fortunately, this algorithm is much easier to implement and debug than multi-layer perceptron :).
I will push a test file soon.

@ogrisel , @larsmans
What I find confusing is that the white region of the color map background is not aligned with the 0-valued decision function. The following fixed values for the `vmin` and `vmax` get the color map centered on zero while yielding good colormap values for the 3 plots:

```
diff --git a/examples/neural_networks/plot_weighted_elm.py b/examples/neural_networks/plot_weighted_elm.py
index c901c10..13a5146 100644
--- a/examples/neural_networks/plot_weighted_elm.py
+++ b/examples/neural_networks/plot_weighted_elm.py
@@ -30,7 +30,8 @@ def plot_decision_function(clf, axis, title):
 
     axis.imshow(Z, interpolation='nearest',
                 extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect='auto',
-                origin='lower', cmap=plt.cm.PuOr_r)
+                origin='lower', cmap=plt.cm.PuOr_r,
+                vmin=-15, vmax=15)
     axis.contour(xx, yy, Z, levels=[0], linewidths=2,
                  linetypes='--', color='b')
     axis.scatter(X[:, 0], X[:, 1], s=20, c=y, cmap=plt.cm.Paired)
```