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
Hi @agramfort , I am using the scaling scheme given here, [Xavier10] http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_GlorotB10.pdf
it is giving me better results than otherwise - cleaner plots and usually higher score.

Since I am adding the reLU activation function, I will add an `else` clause that sets the interval as `1./np.sqrt(n_features)`, a popular initialization method as claimed by the paper. Thanks