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
I think this should parameterization is overly complex / non-intuitive. Instead I suggest to:

- get rid of the constructor param `weight_scale="auto"` default value and instead use `weight_scale=1.` by default.
- rename the local variable `weight_scale` to `weight_init_bound` (just this local variable in the `_fit` method, not the `self.weight_scale` constructor parameter itself)
- do `self.coef_hidden_ *= self.weight_scale` and `self.intercept_hidden_ *= self.weight_scale` if `self.weight_scale != 1.`

The docstring will have to be updated. I hope that the examples will still mostly work fine.