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
No, I mean if you already have a runtime array that is bigger than your memory, it means that you're already using virtual memory (swapping on the hard drive).  I guess the point works if your X itself fits in memory, but the auxiliary data structures used by the ELM fit function (e.g. H) wouldn't fit. This might be the case if X is sparse.

I don't have an understanding of the tradeoffs of the implementation. It would be nice if you could help build such an intuition with these docs. Do you know what I mean?