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
Yes, it's much cleaner that way :-)
```python
# Run the least-square algorithm on the whole dataset
if self.algorithm == 'standard':
          batch_size = n_samples
# Run the recursive least-square algorithm on mini-batches
elif self.algorithm == 'recursion_lsqr':
            batch_size = np.clip(self.batch_size, 1, n_samples)
```
But, wouldn't extracting the first batch out require calling,
```python
 H_batch = self._compute_hidden_activations(X[batch_slice])
```
twice: one inside the loop one outside?
Thanks