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 thought about that but one reason is in executing this statement,
```python
 # This outputs a warning when a 1d array is expected
if y.ndim == 2 and y.shape[1] == 1:
            y = column_or_1d(y, warn=True)
```
Since `y` will be transformed by `labelBinarizer` before it reaches here. `y = column_or_1d(y, warn=True)` will give a warning in every run. And this statement is necessary to have for passing Travis.