[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
I would add a note here to warn the user that this probability estimate is not callibrated: the model optimizes a penalized objective least squares objective on the One Vs Rest binary encoding of the class membership. It is likely that callibration via Platt scaling or Isotonic regression would yield more accurate probability estimates but this is not yet implemented in scikit-learn.