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
@ogrisel  thanks for your explanation.

So, as I understand from the code below,
```python
1.   elm = ELMRegressor(warm_start=True)
2.   elm.fit(X1, y1)
3.   elm.fit(X2, y2)
```
`elm` in line 3 should have the same initial input-to-hidden weight values as `elm` in line 2, but both of them have to learn the hidden-to-output weights from scratch, right?
This is because unlike `mlp`, `elm` does not update on the hidden-to-output weights using previous solutions, unless if it trains through batches.
Thanks.