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
Sorry, it wouldn't be `ridge_regression` if we choose the content of `A` and `B`. 
I believe we can move the recursion algorithm from `ELM` to `ridge_regression` to have recursive ridge regression. It would have a public attribute - equivalent to `self._HT_H_accumulated`.
What do you think?

References:
1) http://stats.stackexchange.com/questions/81740/recursive-online-regularised-least-squares-algorithm
2) http://www.inf.ed.ac.uk/teaching/courses/mlsc/Notes/Lecture6/MLSC_Lec6.pdf