[MRG] 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
This is bad practice to split the scaled data: one should split first and then rescale:

```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4,
                                       random_state=1)
scaler = StandardScaler()
# find mean and standard deviation of each feature on the training set
# only for scaling
X_train = scaler.fit_transform(X_train)

# apply the same transformation on the test set
X_test = scaler.transform(X_test)
```

This should not impact the results much but it's important that scikit-learn example do no advertise code snippet that might cause data snooping issues.