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
Wait, why does this function exist? This is usually implemented for models that work in log-space and make it more natural to return the log probas, in which case getting normalized probability values is more expensive. It's not the case here.

If I see an estimator with `predict_proba` and `predict_log_proba` I'd expect the latter to be more efficient, not the other way around.