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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