As an aside Lars - I'd actually love to see the recepy, if you don't mind
putting up a gist or notebook.
On Wed, Feb 26, 2014 at 1:29 PM, Lars Buitinck <larsm...@gmail.com> wrote:
> 2014-02-25 7:52 GMT+01:00 Gael Varoquaux <gael.varoqu...@normalesup.org>:
> >> Extreme learning machine: theory and applications has 1285 citations
> >> and it got published in 2006; a large number of citations for a fairly
> >> recent article. I believe scikit-learn could add such an interesting
> >> learning algorithm along with its variations (weighted ELMs, sequential
> >> ELMS, etc.)
> >
> > It does sound like a possible candidate for inclusion.
>
> We have a PR that implements them, but in too convoluted a way. My
> personal choice for implementing these would be a transformer doing a
> random projection + nonlinear activation. That way, you can stack any
> linear model on top (think SGDClassifier for large-scale work) and get
> a basic ELM. I've toyed with this variant before (typing this from
> memory):
>
> class RandomHiddenLayer(BaseEstimator, TransformerMixin):
> def __init__(self, n_components=100, random_state=None):
> self.n_components = n_components
> self.random_state = random_state
> def fit(self, X, y=None):
> random_state = check_random_state(self.random_state)
> self.components_ = random_state.randn(n_components, X.shape[1])
> return self
> def transform(self, X):
> return np.tanh(safe_sparse_dot(X, self.components_.T))
>
> Now, make_pipeline(RandomHiddenLayer(), SGDClassifier()) is an ELM
> except with regularized hinge loss instead of least squares. I guess
> LDA can be used to get the "real" ELM.
>
> I recently implemented baseline RBF networks in pretty much the same
> way: k-means + RBF kernel + linear classifier. I didn't submit a PR
> because it's just a pipeline of existing components.
>
> >> Chances are the Multi-layer perceptron PR would be completed before the
> >> summer, so it won't be included in the GSoC proposal.
> >
> >> In order not to get into a scope creep, I compiled the following list of
> >> algorithms to be proposed for the GSoC 2014,
> >
> >> 1) Extreme Learning Machines (
> http://sentic.net/extreme-learning-machines.pdf)
> >> 1a) Weighted Extreme Learning Machines
> >> 1b) Sequential Extreme Learning machines
>
> Does sequential mean for sequence data?
>
>
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