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 think this should be `y = \beta \cdot \sigma(W^T \cdot X + b)` and explain that `\sigma` is the non-linear, component-wise activation function.

Also the picture in figure 1 displays a bias (intercept) for the output layer. You don't have an intercept in the output layer in this implementation. Have the original ELM papers intercepts on the output or not?