[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
Why don't you use the default `activation='relu'` anymore? If you want to stick with `tanh` you should make it more explicit in the description of the example and the title of the plot.

In the description we could write:

```
Note: this example uses ELM with the `tanh` activation function. Small `weight_scale` values tend to shrink the inputs of the activation function and make it work in a close to linear regime. As the output layer is linear the resulting full model behaves as a linear model for small values of the `weight_scale` hyperparameter.

On the other hand, when `weight_scale` is very large the hidden layer activations have a high probability to be binarized to values close to the saturation levels of the `tanh` function: `-1` or `1`. This renders the model as highly non linear.

The strictly linear mode of ELM would not have been observed for `activation="relu"` and small values of `weight_scale`. Therefore `activation="relu"` renders the model less sensitive to the `weight_scale` hyperparameter in general although high values for`weight_scale` still lead to more variance / overfitting.
```