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 think he meant that the Ling paper should be first because it introduces
the architecture and is older. The Zong paper seems to extend upon the
Liang paper


On Wed, Aug 13, 2014 at 9:07 AM, Issam H. Laradji <notifications@github.com>
wrote:

> In sklearn/neural_network/extreme_learning_machines.py:
>
> > +        Class labels for each output.
> > +
> > +    `n_outputs_` : int,
> > +        Number of output neurons.
> > +
> > +    References
> > +    ----------
> > +    Zong, Weiwei, Guang-Bin Huang, and Yiqiang Chen.
> > +        "Weighted extreme learning machine for imbalance learning."
> > +        Neurocomputing 101 (2013): 229-242.
> > +
> > +    Liang, Nan-Ying, et al.
> > +        "A fast and accurate online sequential learning algorithm for
> > +        feedforward networks." Neural Networks, IEEE Transactions on
> > +        17.6 (2006): 1411-1423.
> > +        http://www.ntu.edu.sg/home/egbhuang/pdf/OS-ELM-TNN.pdf
>
> By natural you mean the order should be alphabetical order, right?
> Since *L* in Liang comes before *Z* in Zong, then their references should
> be switched?
> Thanks.
>
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