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
It's just the algebraic representation that dictates which variables need accumulation over the iterations, which is given as,

`beta_{i+1} = K_{i+1}^{-1}H{i+1}^T(y{i+1} - H_{i+1} * beta_{i})`

where K_{i+1} is `H_accumulated` - the variable that accumulates `H`, H_{i+1} is the hidden activations of the current batch,  `y{i+1}` is `y` of the current batch, and `beta_{i}` is `self.coef_output_` of the previous batch