Hi Mathieu,
The regularized version is fundamentally different from the
non-regularized version, in that it uses the derivative of the objective
function for which it solves using least-square solution. The basic ELM
version is classic; so, it wouldn't hurt having them both :). Further,
it is not clear that the regularized version always perform better. And,
many extentions of ELM assume the non-regularized version - like
kernel-based ELMs and Sequential ELM.
It shouldn't take two weeks, sorry. After implementing the
non-regularized version it would take two more days at worst.
Since I am removing sparse auto-encoders from the proposal, I will add
two other variants of Extreme Learning Machines,
1) Weighted Extreme Learning Machines for Imbalanced Data
2) Kernel-Based Extreme Learning Machines, with,
2a) Radial Basis Function Kernel
2b) Polynomial kernel
If there is time I will implement an Extreme Learning Machines version
that increments hidden neurons without recalculating the whole
least-square solution [1]. In other words, it can quickly find the best
number of hidden neurons for a particular problem.
Thanks.
[1]Error minimized*extreme learning machine*with growth of hidden nodes
and incremental*learning*
<http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5161346>
On 3/21/2014 5:42 AM, Mathieu Blondel wrote:
Naive questions from someone who knows nothing about ELM. What's the
motivation for implementing both non-regularized and regularized ELM?
If the former tends to overfit, I would keep only the latter. And why
do you need 2 weeks for implementing the regularized variant? Is the
algorithm fundamentally different from the non-regularized variant?
Mathieu
On Fri, Mar 21, 2014 at 4:44 AM, Issam <[email protected]
<mailto:[email protected]>> wrote:
Hi all,
I uploaded the proposal for Neural Networks to melange, here is
the public link.
http://www.google-melange.com/gsoc/proposal/public/google/gsoc2014/issamou/5668600916475904
Thank you.
Regards,
~Issam
On 3/20/2014 10:47 AM, Jaidev Deshpande wrote:
On Thu, Mar 20, 2014 at 5:54 AM, Issam <[email protected]
<mailto:[email protected]>> wrote:
Hi all,
I uploaded the Neural Network proposal to this link,
https://github.com/scikit-learn/scikit-learn/wiki/GSoC-2014:-Extending-Neural-Networks-Module-for-Scikit-learn
Please see if it is detailed enough as a promising proposal.
Thank you.
~Issam
On 3/19/2014 9:00 PM, Jaidev Deshpande wrote:
On Sat, Mar 15, 2014 at 6:59 PM, Issam <[email protected]
<mailto:[email protected]>> wrote:
Thanks Olivier, I will upload the proposal very soon.
While doing so, I will strengthen my proposal by
implementing a basic
version of each of the proposed algorithms, which I will
cite in my
proposal.
Cheers. :)
Hi Issam,
What's the update on your proposal? I don't mean to rush you
at all, you're probably working hard on the proposal as we
speak, this is just a gentle bump.
All the best.
On 3/14/2014 5:38 PM, Olivier Grisel wrote:
> Issam if I am not mistaken you have not written an
official proposal
> for this GSoC application.
>
> If you are still interested, there is an official
template to follow
> for PSF affiliated sub-projects (such as scikit-learn):
>
>
https://wiki.python.org/moin/SummerOfCode/ApplicationTemplate2014
>
> You can also have a look at Manoj's submission:
>
>
https://github.com/scikit-learn/scikit-learn/wiki/GSoC-2014-Application:-Improved-Linear-Models
>
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Hi Issam,
Looks OK at first glance, but please add it as a gist. Those are
much easier to comment on.
Thanks
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