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
BTW the content of this `for` loop is an implementation for incremental (out-of-core) ridge regression. I think it might be interesting to factor it out in a helper function so as to reuse it to implement a `partial_fit` method for the `Ridge*` estimators in `sklearn.linear_model.ridge`.

Any opinion on the relevance of such a refactoring @mblondel @eickenberg @fabianp? The only road block I see is the lack of support for intercept fitting in this incremental implementation of ridge.

If deemed relevant we can maybe be addressed in a later PR though.