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 would replace the above code by

```
from sklearn.utils.multiclass import unique_labels
from sklearn.utils.fixes import in1d
...


if self.classes_ is None or not warm_start:
    y = self.label_binarizer_.fit_transform(y)
    self.classes_ = self.label_binarizer_.classes_
else:
    classes_ = unique_labels(y)
    if not np.all(in1d(unique_labels(y), self.classes)):
        raise ValueError(...)

    y = self.label_binarizer_.transform(y)

    sample_weight = compute_sample_weight(self.class_weight, self.classes_, y)
```
```