On 2/5/2013 10:24 AM, Olivier Grisel wrote:
> Actually after reading @larsmans implementation I think we could
> indeed start to investigate independently in pure python code and
> later think whether it's worth to make it more interoperable with the
> cython code of the MLP pull request.
All right.
> If you want to start working on a PR, please read this first:
>
> http://scikit-learn.org/dev/developers/index.html#contributing
>
> Also have a look at how other developers are working together on
> existing PRs to get a feel of the process:
>
> https://github.com/scikit-learn/scikit-learn/pulls
>
> Beware, this is a long and painful process but everybody will learn
> during the process (both contributors and reviewers);)
Fair enough, I'll see if I have what it takes...
> Yes, it's very simple to implement. The main thing that needs to be
> solved is the API. Actually, your remark about stacking something else
> than least-squares on top of a random hidden layer made me think:
> would it be a good idea to implement this as a transformer, say
> RandomSigmoid or RandomHiddenLayer?
My existing implementation includes several activation functions for
an ELMRegressor/ELMClassifier pair of objects. I was just about to
add an option to mix activation functions in the hidden layer, which would
fit nicely into a RandomHiddenLayer (or per Olivier's observation about the
random kitchen sinks a RandomKitchenSink) under feature extraction or
kernel approximation.
Olivier wrote:
> Argl. This does not feel like linear model model at all. I would
> rather put the transformer in sklearn.kernel_approximation .
I could implement the RandomHiddenLayer as a feature transformer and
then reimplement the classifier/regressor to use it.
- David
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