Yes, we are aware of the formal complaint surrounding Extreme Learning
Machines. Essentially the same algorithm was published in a *1992 *paper
(http://homepage.tudelft.nl/a9p19/papers/icpr_92_random.pdf), *neural
networks with random weights* - more than 10 years before ELMs came to
light.
We are planning to change the name of the algorithm from Extreme
learning machine to, say, *RandomNeuralNets* and to credit the authors
of the original idea!
On 2015-05-06 10:17 PM, Sebastian Raschka wrote:
It's a little bit off-topic, and I am really not that much into the literature
to be a judge of that, but just a few days ago there was formal letter of
complaint against ELMs: http://theanonymousemail.com/view/?msg=ZHEZJ1AJ
that I thought you might find interesting.
On May 7, 2015, at 1:10 AM, Joel Nothman <joel.noth...@gmail.com> wrote:
What Sebastian and Ronnie said. Plus: there are multiple off-the-shelf neural
net pull requests in the process of review, notably those by Issam Laradji for
GSoC 2014. Extreme Learning Machines and Multilayer Perceptrons should be
merged Real Soon Now.
On 7 May 2015 at 14:58, Ronnie Ghose <ronnie.gh...@gmail.com> wrote:
neural nets are already well supported in other python libraries and don't fit
the current transformer model that scikit-learn uses
On Thu, May 7, 2015 at 12:55 AM, Sebastian Raschka <se.rasc...@gmail.com> wrote:
I am not one of the core developers, just a typical user, but although I think that neural nets
would be a nice addition, I have to admit that I wouldn't count them as top priority. I think that
in applications, neural networks require far more flexibility for tweaking than the
"classic" off-the-shelve learning algorithms currently implemented in scikit-learn. I
think that it really requires a lot of planning to implement them in a way that allows a user
certain flexibility. To me, neural nets are more of an "research tool" in contrast to the
currently implemented algos in scikit-learn. I really would like to see some way of implementing
frameworks for neural networks in some useful way in scikit-learn, but I can understand that it
would really require a different API, a lot of planning, and a lot of work. Also, there are many
attempts to implement neural nets already, like pylearn2, lasagne, and all the other theano wrappers
On May 6, 2015, at 11:15 PM, 赵孽 <snakehunt2...@gmail.com> wrote:
I used to seeking neural network algorithms in sklearn, but I just found a RBF
in it.
There are plenty of Neural Network algorithms, why dose we only support RBF
which is not even a typical neural network ?
I thought the neural networks should be the largest family amount sklearn
algoritms, but it is far smaller than embedings, far smaller than SVMs.
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