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
Good point.
In fact, using `partial_fit` to read the dataset from disk in chunks follows the exact same procedure as training the algorithm on the dataset in batches. Maybe I should set `batch_size=200` by default and remove this tip?
Since whether `batch_size=50` or `batch_size=n_samples`, the end results are exactly the same.

Can you please elaborate on this sentence,
>Because if X doesn't fit in memory, you're already swapping by the time mini batches are formed.

Do you mean that `X`, if doesn't fit in memory, will automatically be broken down into mini batches?

Thanks.